BDLSMOTE: The borderline-SMOTE algorithm.

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

Description

BDLSMOTE over-samples the input data using either borderline-SMOTE1 or borderline-SMOTE2 algorithms.

Usage

BDLSMOTE(data, perc_min = 50, perc_over = NULL, over_replace = FALSE,
  k = 5, m = 2 * (k + 1), borderline = 1, 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.
over_replace	A logical value indicating whether the neighbours picked from the k nearest neighbours should be picked with or without replacement.
k	Number of nearest neighbours to compute for each example in the minority class.
m	Number of neighbours used to decide whether a minority example is borderline or not. The authors call the set of all borderline examples the DANGER set.
borderline	Select between borderline-SMOTE1 and borderline-SMOTE2. Possible values are 1 and 2.
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

Borderline-SMOTE{1, 2} algorithms work similarly to SMOTE, however, they only synthesise new examples using the minority examples that are borderline.

Value

A data frame containing a more balanced version of the input data after over-sampling with either borderline-SMOTE1 or borderline-SMOTE2.

References

Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing (pp. 878-887). Springer Berlin Heidelberg.

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