NCL: The Neighbourhood Cleaning Rule algorithm.

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

Description

NCL removes examples from majority class that are either misclassified by their k nearest neighbours or contributed to the misclassification of examples from minority class.

Usage

NCL(data, k = 3, 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.
k	Number of nearest neighbours to take into account.
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.

Value

A data frame containing a clean version of the input data set after application of the Neighbourhood Cleaning Rule algorithm.

References

Laurikkala, J. (2001, July). Improving identification of difficult small classes by balancing class distribution. In Conference on Artificial Intelligence in Medicine in Europe (pp. 63-66). Springer, Berlin, Heidelberg.

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