SMO-C: SMO_C KEEL Classification Algorithm

In RKEEL: Using 'KEEL' in R Code

SMO_C KEEL Classification Algorithm

Description

SMO_C Classification Algorithm from KEEL.

Usage

SMO_C(train, test, C, toleranceParameter, epsilon,
   RBFKernel_gamma, normalized_PolyKernel_exponent,
   normalized_PolyKernel_useLowerOrder, PukKernel_omega,
   PukKernel_sigma, StringKernel_lambda,
   StringKernel_subsequenceLength,
   StringKernel_maxSubsequenceLength, StringKernel_normalize,
   StringKernel_pruning, KernelType, FitLogisticModels,
   ConvertNominalAttributesToBinary, PreprocessType, seed)

Arguments

train	Train dataset as a data.frame object
test	Test dataset as a data.frame object
C	C. Default value = 1.0
toleranceParameter	toleranceParameter. Default value = 0.001
epsilon	epsilon. Default value = 1.0e-12
RBFKernel_gamma	RBFKernel_gamma. Default value = 0.01
normalized_PolyKernel_exponent	normalized_PolyKernel_exponent. Default value = 1
normalized_PolyKernel_useLowerOrder	normalized_PolyKernel_useLowerOrder. Default value = FALSE
PukKernel_omega	PukKernel_omega. Default value = 1.0
PukKernel_sigma	PukKernel_sigma. Default value = 1.0
StringKernel_lambda	StringKernel_lambda. Default value = 0.5
StringKernel_subsequenceLength	StringKernel_subsequenceLength. Default value = 3
StringKernel_maxSubsequenceLength	StringKernel_maxSubsequenceLength. Default value = 9
StringKernel_normalize	StringKernel_normalize. Default value = FALSE
StringKernel_pruning	StringKernel_pruning. Default value = "None"
KernelType	KernelType. Default value = "PolyKernel"
FitLogisticModels	FitLogisticModels. Default value = FALSE
ConvertNominalAttributesToBinary	ConvertNominalAttributesToBinary. Default value = TRUE
PreprocessType	PreprocessType. Default value = "Normalize"
seed	Seed for random numbers. If it is not assigned a value, the seed will be a random number

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::SMO_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

RKEEL documentation built on Sept. 15, 2023, 1:08 a.m.