SMOTE: The SMOTE algorithm.

In RomeroBarata/bimba: Sampling Algorithms for Two-Class Imbalanced Data Sets

Description

SMOTE returns a more balanced version of a data set after application of the SMOTE algorithm.

Usage

SMOTE(data, perc_min = 50, perc_over = NULL, k = 5,
  over_replace = FALSE, classes = NULL)

Arguments

data	A data frame containing the predictors and the outcome. The predictors must be numeric and the outcome must be both a binary valued factor and the last column of data.
perc_min	The desired % size of the minority class relative to the whole data set. For instance, if perc_min = 50 the returned data set is balanced. perc_min is ignored if perc_over is specified.
perc_over	% of examples to append to the input data set relative to the size of the minority class. For instance, if perc_over = 100 the minority class doubles in size. If specified, perc_min is ignored.
k	Number of nearest neighbours to compute for each example in the minority class.
over_replace	A logical value indicating whether the neighbours picked from the k nearest neighbours should be picked with or without replacement.
classes	A named vector identifying the majority and the minority classes. The names must be "Majority" and "Minority". This argument is only useful if the function is called inside another sampling function.

Details

SMOTE is an over-sampling algorithm that synthesises new examples in the line segment joining two close minority class examples.

Value

A data frame containing a more balanced version of the input data set after application of the SMOTE algorithm.

References

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.

RomeroBarata/bimba documentation built on May 17, 2019, 8:03 a.m.