Instance Selection for Monotonic Classification

Selects the N most relevant instances from ordinal and monotonic dataset, using a three-steps algorithm.

iselector(traindata, trainlabels, candidates, collisions, kEdition)

Arguments

traindata	Training data of numeric type without labels.
trainlabels	A vector of numeric tags for each instance of training data.
candidates	Rate of the best candidates to be selected.
collisions	Minimal rate of collisions permitted to stop the removal process.
kEdition	Maximum number of nearest neighborgs to consider.

Value

A reduced dataset with the selected instances and its labels.

Examples

dattrain<-read.table("train_balance-scale.0", sep=" ")
#> Warning: cannot open file 'train_balance-scale.0': No such file or directory
#> Error in file(file, "rt"): cannot open the connection
trainlabels<-dattrain[,ncol(dattrain)]
#> Error in eval(expr, envir, enclos): object 'dattrain' not found
traindata=dattrain[,-ncol(dattrain)]
#> Error in eval(expr, envir, enclos): object 'dattrain' not found
selected<-iselector(traindata,trainlabels,0.01,0.01,5)
#> Error in iselector(traindata, trainlabels, 0.01, 0.01, 5): object 'trainlabels' not found