rwo: Random walk oversampling

In imbalance: Preprocessing Algorithms for Imbalanced Datasets

Description

Generates synthetic minority examples for a dataset trying to preserve the variance and mean of the minority class. Works on every type of dataset.

Usage

rwo(dataset, numInstances, classAttr = "Class")

Arguments

dataset	data.frame to treat. All columns, except classAttr one, have to be numeric or coercible to numeric.
numInstances	Integer. Number of new minority examples to generate.
classAttr	character. Indicates the class attribute from dataset. Must exist in it.

Details

Generates numInstances new minority examples for dataset, adding to the each numeric column of the j-th example its variance scalated by the inverse of the number of minority examples and a factor following a N(0,1) distribution which depends on the example. When the column is nominal, it uses a roulette scheme.

Value

A data.frame with the same structure as dataset, containing the generated synthetic examples.

References

Zhang, Huaxiang; Li, Mingfang. Rwo-Sampling: A Random Walk Over-Sampling Approach To Imbalanced Data Classification. Information Fusion 20 (2014), p. 99–116.

Examples

data(iris0)

newSamples <- rwo(iris0, numInstances = 100, classAttr = "Class")

imbalance documentation built on April 14, 2020, 6:02 p.m.