Minimization of Gini impurity via connections with the k-means problem
Data Structures and Algorithms
2018-10-02 v1 Computational Complexity
Machine Learning
Abstract
The Gini impurity is one of the measures used to select attribute in Decision Trees/Random Forest construction. In this note we discuss connections between the problem of computing the partition with minimum Weighted Gini impurity and the -means clustering problem. Based on these connections we show that the computation of the partition with minimum Weighted Gini is a NP-Complete problem and we also discuss how to obtain new algorithms with provable approximation for the Gini Minimization problem.
Keywords
Cite
@article{arxiv.1810.00029,
title = {Minimization of Gini impurity via connections with the k-means problem},
author = {Eduardo Sany Laber and Lucas Murtinho},
journal= {arXiv preprint arXiv:1810.00029},
year = {2018}
}