WERCSClassif | R Documentation |
This function handles imbalanced classification problems using the importance/relevance provided to re-sample the data set. The relevance is used to introduce replicas of the most important examples and to remove the least important examples. This function combines random over-sampling with random under-sampling which are applied in the problem classes according to the corresponding relevance.
WERCSClassif(form, dat, C.perc = "balance")
form |
A formula describing the prediction problem |
dat |
A data frame containing the original (unbalanced) data set |
C.perc |
A list containing the percentage(s) of random under- or over-sampling to apply to each class. The over-sampling percentage is a number above 1 while the under-sampling percentage should be a number below 1. If the number 1 is provided for a given class then that class remains unchanged. Alternatively it may be "balance" (the default) or "extreme", cases where the sampling percentages are automatically estimated. |
The function returns a data frame with the new data set resulting from the application of the importance sampling strategy.
Paula Branco paobranco@gmail.com, Rita Ribeiro rpribeiro@dcc.fc.up.pt and Luis Torgo ltorgo@dcc.fc.up.pt
RandUnderClassif, RandOverClassif
data(iris)
# generating an artificially imbalanced data set
ir <- iris[-c(51:70,111:150), ]
IS.ext <-WERCSClassif(Species~., ir, C.perc = "extreme")
IS.bal <-WERCSClassif(Species~., ir, C.perc = "balance")
myIS <-WERCSClassif(Species~., ir, C.perc = list(setosa = 0.2,
versicolor = 2,
virginica = 6))
# check the results
table(ir$Species)
table(IS.ext$Species)
table(IS.bal$Species)
table(myIS$Species)
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