Description Usage Arguments Value Examples
View source: R/featureSelection.R
Feature selection wrapper
1 | feature_selection(dataset, method, class_attr = NULL, exclude = NULL, ...)
|
dataset |
we want to do feature selection on |
method |
selected method of feature selection |
class_attr |
|
exclude |
|
... |
Further arguments for |
The treated dataset (either with noisy instances replaced or erased)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | library("smartdata")
library("rpart")
data(ecoli1, package = "imbalance")
data(HouseVotes84, package = "mlbench")
# Extracted from FSelector::best.first.search documentation
evaluator <- function(subset) {
k <- 5
splits <- runif(nrow(iris))
results = sapply(1:k, function(i) {
test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
train.idx <- !test.idx
test <- iris[test.idx, , drop=FALSE]
train <- iris[train.idx, , drop=FALSE]
tree <- rpart(FSelector::as.simple.formula(subset, "Species"), train)
error.rate = sum(test$Species != predict(tree, test, type="c")) / nrow(test)
return(1 - error.rate)
})
print(subset)
print(mean(results))
return(mean(results))
}
super_iris <- feature_selection(iris, "Boruta", class_attr = "Species")
super_iris <- feature_selection(iris, "chi_squared",
class_attr = "Species", num_features = 3)
# Pick 3 attributes from the continuous ones
super_ecoli <- feature_selection(ecoli1, "information_gain",
class_attr = "Class", num_features = 3)
super_ecoli <- feature_selection(ecoli1, "gain_ratio",
class_attr = "Class", num_features = 3)
super_ecoli <- feature_selection(ecoli1, "sym_uncertainty",
class_attr = "Class", num_features = 3)
super_votes <- feature_selection(HouseVotes84, "oneR", exclude = c("V1", "V2"),
class_attr = "Class", num_features = 3)
super_votes <- feature_selection(iris, "RF_importance", class_attr = "Species",
num_features = 3, type = 2)
super_iris <- feature_selection(iris, "best_first_search", exclude = "Species",
eval_fun = evaluator)
super_iris <- feature_selection(iris, "forward_search", exclude = "Species",
eval_fun = evaluator)
super_iris <- feature_selection(iris, "backward_search", exclude = "Species",
eval_fun = evaluator)
super_iris <- feature_selection(iris, "cfs", class_attr = "Species")
super_iris <- feature_selection(iris, "consistency", class_attr = "Species")
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