Package 'RKEEL'

Description

ABB_IEP_FS Preprocess Algorithm from KEEL.

Usage

ABB_IEP_FS(train, test, seed)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::ABB_IEP_FS(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

AdaBoost_I Imbalanced Classification Algorithm from KEEL.

Usage

AdaBoost_I(train, test, pruned, confidence, instancesPerLeaf,
   numClassifiers, algorithm, trainMethod, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::AdaBoost_I(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

AdaBoostNC_C Classification Algorithm from KEEL.

Usage

AdaBoostNC_C(train, test, pruned, confidence, instancesPerLeaf,
   numClassifiers, algorithm, trainMethod, lambda, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::AdaBoostNC_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Alatasetal_A Association Rules Algorithm from KEEL.

Usage

Alatasetal_A(dat, seed, NumberofEvaluations, InitialRandomChromosomes,
  rDividingPoints, TournamentSize, ProbabilityofCrossover,
  MinimumProbabilityofMutation, MaximumProbabilityofMutation,
  ImportanceofRulesSupport, ImportanceofRulesConfidence,
  ImportanceofNumberofInvolvedAttributes, ImportanceofIntervalsAmplitude,
  ImportanceofNumberofRecordsAlreadyCovered, AmplitudeFactor)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::Alatasetal_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

Alcalaetal_A Association Rules Algorithm from KEEL.

Usage

Alcalaetal_A(dat, seed, NumberofEvaluations, PopulationSize, NumberofBitsperGene, 
  DecreasingFactorofLthresholdNOTUSED, FactorforParentCentricBLXCrossover, 
  NumberofFuzzyRegionsforNumericAttributes, UseMaxOperatorfor1FrequentItemsets, 
  MinimumSupport, MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::Alcalaetal_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

AllKNN_TSS Preprocess Algorithm from KEEL.

Usage

AllKNN_TSS(train, test, k, distance)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::AllKNN_TSS(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

AllPosible_MV Preprocess Algorithm from KEEL.

Usage

AllPosible_MV(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::AllPosible_MV(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

ANR_F Preprocess Algorithm from KEEL.

Usage

ANR_F(train, test, seed)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("zoo")
data_test <- RKEEL::loadKeelDataset("zoo")
  
#Create algorithm
algorithm <- RKEEL::ANR_F(data_train, data_test)
  
#Run algorithm
algorithm$run()
  
#See results
algorithm$preprocessed_test

Description

Apriori_A Association Rules Algorithm from KEEL.

Usage

Apriori_A(dat, NumberofPartitionsforNumericAttributes, MinimumSupport, 
  MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::Apriori_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

ART_C Classification Algorithm from KEEL.

Usage

ART_C(train, test)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::ART_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Class inheriting of KeelAlgorithm, to common methods for all KEEL Association Rules Algorithms. The specific association rules algorithms must inherit of this class.

The run() method receives three parameters. The folderPath parameter indicates where to place the folder with the experiments if wanted. If it is not indicated, the folder is placen ind a temporary random directory and then removed. If indicated, the experiment folder is not removed. The expUniqueName parameter indicates the name of the experiment folder. If not indicated, it is a random name. If indicated, ensure that the name is unique in the previously indicated folder. The javaOptions parameter indicates, if wanted, extra parameters to the java command line, as for example the maximum memory allowed by java.

Description

Class inheriting of ClassificationAlgorithm, to common methods for Associative Classification Algorithms.

The run() method receives three parameters. The folderPath parameter indicates where to place the folder with the experiments if wanted. If it is not indicated, the folder is placen ind a temporary random directory and then removed. If indicated, the experiment folder is not removed. The expUniqueName parameter indicates the name of the experiment folder. If not indicated, it is a random name. If indicated, ensure that the name is unique in the previously indicated folder. The javaOptions parameter indicates, if wanted, extra parameters to the java command line, as for example the maximum memory allowed by java.

Description

Bayesian_D Preprocess Algorithm from KEEL.

Usage

Bayesian_D(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::Bayesian_D(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

BNGE_C Classification Algorithm from KEEL.

Usage

BNGE_C(train, test, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::BNGE_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Bojarczuk_GP_C Classification Algorithm from KEEL.

Usage

Bojarczuk_GP_C(train, test, population_size, max_generations,
   max_deriv_size, rec_prob, copy_prob, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::Bojarczuk_GP_C(data_train, data_test)
algorithm <- RKEEL::Bojarczuk_GP_C(data_train, data_test, population_size=5, max_generations=10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

BSE_C Classification Algorithm from KEEL.

Usage

BSE_C(train, test, k, distance)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::BSE_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

C_SVM_C Classification Algorithm from KEEL.

Usage

C_SVM_C(train, test, KernelType, C, eps, degree, gamma, coef0,
   nu, p, shrinking, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::C_SVM_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

C45_C Classification Algorithm from KEEL.

Usage

C45_C(train, test, pruned, confidence, instancesPerLeaf)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::C45_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

C45Binarization_C Classification Algorithm from KEEL.

Usage

C45Binarization_C(train, test, pruned, confidence, instancesPerLeaf,
   binarization, scoreFunction, bts)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")
  
#Create algorithm
algorithm <- RKEEL::C45Binarization_C(data_train, data_test)
  
#Run algorithm
algorithm$run()
  
#See results
algorithm$testPredictions

Description

C45Rules_C Classification Algorithm from KEEL.

Usage

C45Rules_C(train, test, confidence, itemsetsPerLeaf, threshold,
   seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::C45Rules_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CamNN_C Classification Algorithm from KEEL.

Usage

CamNN_C(train, test, k)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CamNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CART_C Classification Algorithm from KEEL.

Usage

CART_C(train, test, maxDepth)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CART_C(data_train, data_test, maxDepth=3)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CART_R Regression Algorithm from KEEL.

Usage

CART_R(train, test, maxDepth)

Arguments

Value

A data.frame with the actual and predicted values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::CART_R(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CBA_C Associative Classification Algorithm from KEEL.

Usage

CBA_C(train, test, min_support, min_confidence, pruning, maxCandidates)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data <- loadKeelDataset("breast")

#Create algorithm
algorithm <- RKEEL::CBA_C(data, data)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CenterNN_C Classification Algorithm from KEEL.

Usage

CenterNN_C(train, test)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CenterNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CFAR_C Classification Algorithm from KEEL.

Usage

CFAR_C(train, test, min_support, min_confidence, threshold,
   num_labels, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CFAR_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CFKNN_C Classification Algorithm from KEEL.

Usage

CFKNN_C(train, test, k, alpha, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CFKNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CHC_C Classification Algorithm from KEEL.

Usage

CHC_C(train, test, pop_size, evaluations, alfa, restart_change,
   prob_restart, prob_diverge, k, distance, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CHC_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Class inheriting of KeelAlgorithm, to common methods for all KEEL Classification Algorithms. The specific classification algorithms must inherit of this class.

The run() method receives three parameters. The folderPath parameter indicates where to place the folder with the experiments if wanted. If it is not indicated, the folder is placen ind a temporary random directory and then removed. If indicated, the experiment folder is not removed. The expUniqueName parameter indicates the name of the experiment folder. If not indicated, it is a random name. If indicated, ensure that the name is unique in the previously indicated folder. The javaOptions parameter indicates, if wanted, extra parameters to the java command line, as for example the maximum memory allowed by java.

Description

Class to calculate and store some results for a ClassificationAlgorithm. It receives as parameter the prediction of a classification algorithm as a data.frame object.

Description

CleanAttributes_TR Preprocess Algorithm from KEEL.

Usage

CleanAttributes_TR(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::CleanAttributes_TR(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

ClusterAnalysis_D Preprocess Algorithm from KEEL.

Usage

ClusterAnalysis_D(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::ClusterAnalysis_D(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

CMAR_C Associative Classification Algorithm from KEEL.

Usage

CMAR_C(train, test, min_confidence, min_support, databaseCoverage)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data <- loadKeelDataset("breast")

#Create algorithm
algorithm <- RKEEL::CMAR_C(data, data)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CNN_C Classification Algorithm from KEEL.

Usage

CNN_C(train, test, k, distance, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CPAR_C Associative Classification Algorithm from KEEL.

Usage

CPAR_C(train, test, delta, min_gain, alpha, rules_prediction)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data <- loadKeelDataset("breast")

#Create algorithm
algorithm <- RKEEL::CPAR_C(data, data)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CPW_C Classification Algorithm from KEEL.

Usage

CPW_C(train, test, beta, mu, ro, epsilon)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CPW_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

CW_C Classification Algorithm from KEEL.

Usage

CW_C(train, test, beta, mu, epsilon)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::CW_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

DecimalScaling_TR Preprocess Algorithm from KEEL.

Usage

DecimalScaling_TR(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::DecimalScaling_TR(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

DecrRBFN_C Classification Algorithm from KEEL.

Usage

DecrRBFN_C(train, test, percent, num_neurons_ini, alfa, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::DecrRBFN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Deeps_C Classification Algorithm from KEEL.

Usage

Deeps_C(train, test, beta)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::Deeps_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Downloads a file from a given mirror and checks its md5 sum. The file is stored in a given path

Usage

downloadFromMirror(mirror, file_path, md5_sum)

Arguments

Value

Returns 1 if the download was successful and -1 otherwise.

Examples

# Download RKEELjars file
dCode = RKEEL::downloadFromMirror("https://personal.us.es/jmoyano1/RKEELjars_1.1.zip", 
  downloadedJarFile, md5_sum)

# Check if the download was successful
if(dCode<0){
  print('There was an error during the download.')
}

Description

DSM_C Classification Algorithm from KEEL.

Usage

DSM_C(train, test, iterations, percentage, alpha_0, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::DSM_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

DT_GA_C Classification Algorithm from KEEL.

Usage

DT_GA_C(train, test, confidence, instancesPerLeaf,
   geneticAlgorithmApproach, threshold, numGenerations,
   popSize, crossoverProb, mutProb, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::DT_GA_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

EARMGA_A Association Rules Algorithm from KEEL.

Usage

EARMGA_A(dat, seed, FixedLengthofAssociationRules, PopulationSize, 
  TotalNumberofEvaluations, DifferenceBoundaryNOTUSED, ProbabilityofSelection, 
  ProbabilityofCrossover, ProbabilityofMutation, 
  NumberofPartitionsforNumericAttributes)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::EARMGA_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

Eclat_A Association Rules Algorithm from KEEL.

Usage

Eclat_A(dat, NumberofPartitionsforNumericAttributes, MinimumSupport, 
  MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::Eclat_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

EPSILON_SVR_R Regression Algorithm from KEEL.

Usage

EPSILON_SVR_R(train, test, KernelType, C, eps, degree, gamma,
   coef0, nu, p, shrinking, seed)

Arguments

Value

A data.frame with the actual and predicted values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::EPSILON_SVR_R(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Falco_GP_C Classification Algorithm from KEEL.

Usage

Falco_GP_C(train, test, population_size, max_generations,
   max_deriv_size, rec_prob, mut_prob, copy_prob, alpha, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::Falco_GP_C(data_train, data_test)
algorithm <- RKEEL::Falco_GP_C(data_train, data_test, population_size = 5, max_generations = 10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FCRA_C Classification Algorithm from KEEL.

Usage

FCRA_C(train, test, generations, pop_size, length_S_C, WCAR,
   WV, crossover_prob, mut_prob, n1, n2, max_iter,
   linguistic_values, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::FCRA_C(data_train, data_test, generations=10, pop_size=10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FPgrowth_A Association Rules Algorithm from KEEL.

Usage

FPgrowth_A(dat, NumberofPartitionsforNumericAttributes, MinimumSupport, 
  MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::FPgrowth_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

FRNN_C Classification Algorithm from KEEL.

Usage

FRNN_C(train, test)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::FRNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FRSBM_R Regression Algorithm from KEEL.

Usage

FRSBM_R(train, test, numrules, sigma, seed)

Arguments

Value

A data.frame with the actual and predicted values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::FRSBM_R(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FURIA_C Classification Algorithm from KEEL.

Usage

FURIA_C(train, test, optimizations, folds, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::FURIA_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FuzzyApriori_A Association Rules Algorithm from KEEL.

Usage

FuzzyApriori_A(dat, NumberofPartitionsforNumericAttributes,
  UseMaxOperatorfor1FrequentItemsets, MinimumSupport, MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset 
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::FuzzyApriori_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

FuzzyFARCHD_C Classification Algorithm from KEEL.

Usage

FuzzyFARCHD_C(train, test, linguistic_values, min_support,
   max_confidence, depth_max, K, max_evaluations, pop_size,
   alpha, bits_per_gen, inference_type, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::FuzzyFARCHD_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FuzzyKNN_C Classification Algorithm from KEEL.

Usage

FuzzyKNN_C(train, test, k, M, initialization, init_k)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::FuzzyKNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

FuzzyNPC_C Classification Algorithm from KEEL.

Usage

FuzzyNPC_C(train, test, M)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::FuzzyNPC_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

GANN_C Classification Algorithm from KEEL.

Usage

GANN_C(train, test, hidden_layers, hidden_nodes, transfer, eta,
   alpha, lambda, test_data, validation_data, cross_validation,
   BP_cycles, improve, tipify_inputs, save_all, elite,
   num_individuals, w_range, connectivity, P_bp, P_param,
   P_struct, max_generations, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::GANN_C(data_train, data_test, hidden_layers=1, 
  hidden_nodes=5, max_generations=5)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

GAR_A Association Rules Algorithm from KEEL.

Usage

GAR_A(dat, seed, NumberofItemsets, TotalNumberofEvaluations, PopulationSize,
  ProbabilityofSelection, ProbabilityofCrossover, ProbabilityofMutation,
  ImportanceofNumberofRecordsAlreadyCovered, ImportanceofIntervalsAmplitude,
  ImportanceofNumberofInvolvedAttributes, AmplitudeFactor, MinimumSupport,
  MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("glass")

#Create algorithm
algorithm <- RKEEL::GAR_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

GENAR_A Association Rules Algorithm from KEEL.

Usage

GENAR_A(dat, seed, NumberofAssociationRules, TotalNumberofEvaluations, 
  PopulationSize, ProbabilityofSelection, ProbabilityofMutation, 
  PenalizationFactor, AmplitudeFactor)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("glass")

#Create algorithm
algorithm <- RKEEL::GENAR_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

GeneticFuzzyApriori_A Association Rules Algorithm from KEEL.

Usage

GeneticFuzzyApriori_A(dat, seed, NumberofEvaluations, PopulationSize, 
  ProbabilityofMutation, ProbabilityofCrossover, ParameterdforMMACrossover, 
  NumberofFuzzyRegionsforNumericAttributes, UseMaxOperatorfor1FrequentItemsets, 
  MinimumSupport, MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::GeneticFuzzyApriori_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

GeneticFuzzyAprioriDC_A Association Rules Algorithm from KEEL.

Usage

GeneticFuzzyAprioriDC_A(dat, seed, NumberofEvaluations, PopulationSize, 
  ProbabilityofMutation, ProbabilityofCrossover, ParameterdforMMACrossover, 
  NumberofFuzzyRegionsforNumericAttributes, UseMaxOperatorfor1FrequentItemsets, 
  MinimumSupport, MinimumConfidence)

Arguments

Details

$run() Run algorith

$showRules(numRules) Show a number of rules. By default all rules.

$getInterestMeasures() Return a data.frame with all interest measures of set rules.

$sortBy(interestMeasure) Order set rules by interest measure.

$writeCSV(fileName, sep) Create CSV file with set rules. Default fileName="rules" sep=","

$writePMML(fileName) Create PMML file with set rules. Default fileName="rules"

$addInterestMeasure(name, colName) Add interest measures to set rules. Some interest measures supported:

"allConfidence" (Omiencinski, 2003)

"crossSupportRatio", cross-support ratio (Xiong et al., 2003)

"lift", interest factor (Brin et al. 1997)

"support", supp (Agrawal et al., 1996)

"addedValue", added Value, AV, Pavillon index, centered confidence (Tan et al., 2002)

"chiSquared", X^2 (Liu et al., 1999)

"certainty", certainty factor, CF, Loevinger (Berzal et al., 2002)

"collectiveStrength"

"confidence", conf (Agrawal et al., 1996)

"conviction" (Brin et al. 1997)

"cosine" (Tan et al., 2004)

"coverage", cover, LHS-support

"confirmedConfidence", descriptive confirmed confidence (Kodratoff, 1999)

"casualConfidence", casual confidence (Kodratoff, 1999)

"casualSupport", casual support (Kodratoff, 1999)

"counterexample", example and counterexample rate

"descriptiveConfirm", descriptive-confirm (Kodratoff, 1999)

"doc", difference of confidence (Hofmann and Wilhelm, 2001)

"fishersExactTest", Fisher's exact test (Hahsler and Hornik, 2007)

"gini", Gini index (Tan et al., 2004)

"hyperLift" (Hahsler and Hornik, 2007)

"hyperConfidence" (Hahsler and Hornik, 2007)

"imbalance", imbalance ratio, IR (Wu, Chen and Han, 2010)

"implicationIndex", implication index (Gras, 1996)

"improvement" (Bayardo et al., 2000)

"jaccard", Jaccard coefficient (Tan and Kumar, 2000)

"jMeasure", J-measure, J (Smyth and Goodman, 1991)

"kappa" (Tan and Kumar, 2000)

"klosgen", Klosgen (Tan and Kumar, 2000)

"kulczynski" (Wu, Chen and Han, 2007; Kulczynski, 1927)

"lambda", Goodman-Kruskal lambda, predictive association (Tan and Kumar, 2000)

"laplace", L (Tan and Kumar 2000)

"leastContradiction", least contradiction (Aze and Kodratoff, 2004

"lerman", Lerman similarity (Lerman, 1981)

"leverage", PS (Piatetsky-Shapiro 1991)

"mutualInformation", uncertainty, M (Tan et al., 2002)

"oddsRatio", odds ratio alpha (Tan et al., 2004)

"phi", correlation coefficient phi (Tan et al. 2004)

"ralambrodrainy", Ralambrodrainy Measure (Ralambrodrainy, 1991)

"RLD", relative linkage disequilibrium (Kenett and Salini, 2008)

"sebag", Sebag measure (Sebag and Schoenauer, 1988)

"support", supp (Agrawal et al., 1996)

"varyingLiaison", varying rates liaison (Bernard and Charron, 1996)

"yuleQ", Yule's Q (Tan and Kumar, 2000)

"yuleY", Yule's Y (Tan and Kumar, 2000)

For more information see ?arules::interestMeasure

Value

A arules class with the Association Rules for both dat dataset.

Examples

#Load KEEL dataset
dat<-RKEEL::loadKeelDataset("car")

#Create algorithm
algorithm <- RKEEL::GeneticFuzzyAprioriDC_A(dat)

#Run algorithm
algorithm$run()

#Rules in format arules
algorithm$rules

#Show a number of rules
algorithm$showRules(2)

#Return a data.frame with all interest measures of set rules
algorithm$getInterestMeasures()

#Add interst measure YuleY to set rules
algorithm$addInterestMeasure("YuleY","yulesY")

#Sort by interest measure lift
algorithm$sortBy("lift")

#Save rules in CSV file
algorithm$writeCSV(paste0(tempdir(), "/myrules"))

Description

Method for getting the attribute lines from data.frame objects

Usage

getAttributeLinesFromDataframes(trainData, testData)

Arguments

Value

Returns a list with the attribute names and types

Examples

iris_train <- RKEEL::loadKeelDataset("iris_train")
iris_test <- RKEEL::loadKeelDataset("iris_test")

attributeLines <- getAttributeLinesFromDataframes(iris_train, iris_test)

Description

Method for knowing the KEEL .jar files path.

Usage

getExePath()

Value

Returns a string with the path of the KEEL .jar files.

Examples

getExePath()

Description

Method that returns a list with the jar names from RKEEL

Usage

getJarList()

Value

Returns a list with the jar names from RKEEL.

Examples

getJarList()

Description

Method for knowing the RunKeel.jar path.

Usage

getJarPath()

Value

Returns a string with the RunKeel.jar path.

Examples

getJarPath()

Description

GFS_AdaBoost_C Classification Algorithm from KEEL.

Usage

GFS_AdaBoost_C(train, test, numLabels, numRules, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::GFS_AdaBoost_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

GFS_GP_R Regression Algorithm from KEEL.

Usage

GFS_GP_R(train, test, numLabels, numRules, popSize, numisland,
   steady, numIter, tourSize, mutProb, aplMut, probMigra,
   probOptimLocal, numOptimLocal, idOptimLocal, nichinggap,
   maxindniche, probintraniche, probcrossga, probmutaga,
   lenchaingap, maxtreeheight, seed)

Arguments

Value

A data.frame with the actual and predicted values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::GFS_GP_R(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

GFS_GSP_R Regression Algorithm from KEEL.

Usage

GFS_GSP_R(train, test, numLabels, numRules, deltafitsap,
   p0sap, p1sap, amplMut, nsubsap, probOptimLocal,
   numOptimLocal, idOptimLocal, probcrossga, probmutaga,
   lenchaingap, maxtreeheight, numItera, seed)

Arguments

Value

A data.frame with the actual and predicted values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::GFS_GSP_R(data_train, data_test)
algorithm <- RKEEL::GFS_GSP_R(data_train, data_test, numRules=2, numItera=10, maxtreeheight=2)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

GFS_LogitBoost_C Classification Algorithm from KEEL.

Usage

GFS_LogitBoost_C(train, test, numLabels, numRules, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::GFS_LogitBoost_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

GFS_RB_MF_R Regression Algorithm from KEEL.

Usage

GFS_RB_MF_R(train, test, numLabels, popSize, generations,
   crossProb, mutProb, seed)

Arguments

Value

A data.frame with the actual and predicted values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::GFS_RB_MF_R(data_train, data_test)
algorithm <- RKEEL::GFS_RB_MF_R(data_train, data_test, popSize = 5, generations = 10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Method for check if a dataset has continuous data

Usage

hasContinuousData(data)

Arguments

Value

Returns TRUE if the dataset has continuous data and FALSE if it has not.

Examples

iris <- RKEEL::loadKeelDataset("iris")
hasContinuousData(iris)

Description

Method for check if a dataset has missing values

Usage

hasMissingValues(data)

Arguments

Value

Returns TRUE if the dataset has missing values and FALSE if it has not.

Examples

iris <- RKEEL::loadKeelDataset("iris")
hasMissingValues(iris)

Description

ID3_C Classification Algorithm from KEEL.

Usage

ID3_C(train, test)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::ID3_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

ID3_D Preprocess Algorithm from KEEL.

Usage

ID3_D(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::ID3_D(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

IF_KNN_C Classification Algorithm from KEEL.

Usage

IF_KNN_C(train, test, K, mA, vA, mR, vR, k)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::IF_KNN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Ignore_MV Preprocess Algorithm from KEEL.

Usage

Ignore_MV(train, test)

Arguments

Value

A data.frame with the preprocessed data for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("car_train")
data_test <- RKEEL::loadKeelDataset("car_test")

#Create algorithm
algorithm <- RKEEL::Ignore_MV(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$preprocessed_test

Description

Class inheriting of ClassificationAlgorithm, to common methods for all KEEL Imbalanced Classification Algorithms. The specific imbalanced-classification algorithms must inherit of this class.

Description

IncrRBFN_C Classification Algorithm from KEEL.

Usage

IncrRBFN_C(train, test, epsilon, alfa, delta, seed)

Arguments

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::IncrRBFN_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

Description

Method for check if a dataset is multi-class

Usage

isMultiClass(data)

Arguments

Value

Returns TRUE if the dataset is multi-class and FALSE if it is not.

Examples

iris <- RKEEL::loadKeelDataset("iris")
isMultiClass(iris)

Description

IterativePartitioningFilter_F Preprocess Algorithm from KEEL.

Usage

IterativePartitioningFilter_F(train, test, numPartitions,
   filterType, confidence, itemsetsPerLeaf, seed)

Arguments