Nothing
resamplingParams <- list(method = "cv", number = 5)
fittingParams <- list(preProc = c("center", "scale"), metric = "Accuracy", tuneGrid = expand.grid(k = seq(1, 10, by = 2)))
wra <- wrapperEvaluator("knn",resamplingParams, fittingParams) # wrapper method
measures <- list(binaryConsistency(), IEConsistency(), IEPConsistency(), roughsetConsistency(), giniIndex(), mutualInformation(), gainRatio(), symmetricalUncertain(), determinationCoefficient(), MDLC(), ReliefFeatureSetMeasure(), wra)
methods <- list(
list(method = sequentialForwardSelection(),
name = 'SFS'),
list(method = sequentialBackwardSelection(),
name = 'SBS'),
list(method = sequentialFloatingForwardSelection(),
name = 'SFFS'),
list(method = sequentialFloatingBackwardSelection(),
name = 'SFBS'),
list(method = LasVegas(),
name = 'LVW'),
list(method = geneticAlgorithm(),
name = 'GA'),
list(method = simulatedAnnealing(),
name = 'SA'),
list(method = whaleOptimization(),
name = 'WOA'),
list(method = antColony(),
name = 'ACO'),
list(method = tabu(),
name = 'TS'),
list(method = hillClimbing(),
name = 'HC')
)
for (method in methods) {
print("")
print("###############################################")
print(paste("Method ",method$name))
print("###############################################")
print("")
print(paste("Method ",method$name))
for (measure in measures) {
print("")
print(" @@@@@@@")
print(paste(" @Measure ",attr(measure,'name')))
print(" @@@@@@@")
print("")
value <- method$method(iris, 'Species',measure)
print(value)
}
}
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