The algorithms select a subset from a ranked attributes.
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a data.frame containing ranks for attributes in the first column and their names as row names
a positive integer in case of
cutoff.k chooses k best attributes
cutoff.k.percent chooses best k * 100% of attributes
cutoff.biggest.diff chooses a subset of attributes which are significantly better than other.
A character vector containing selected attributes.
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data(iris) weights <- information.gain(Species~., iris) print(weights) subset <- cutoff.k(weights, 1) f <- as.simple.formula(subset, "Species") print(f) subset <- cutoff.k.percent(weights, 0.75) f <- as.simple.formula(subset, "Species") print(f) subset <- cutoff.biggest.diff(weights) f <- as.simple.formula(subset, "Species") print(f)
OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed attr_importance Sepal.Length 0.4521286 Sepal.Width 0.2672750 Petal.Length 0.9402853 Petal.Width 0.9554360 Species ~ Petal.Width <environment: 0x45b6b78> Species ~ Petal.Width + Petal.Length + Sepal.Length <environment: 0x45d54d0> Species ~ Petal.Width + Petal.Length <environment: 0x45f6fd0> Warning message: system call failed: Cannot allocate memory
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