Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection
Machine Learning
2023-06-29 v1 Information Theory
math.IT
Machine Learning
Abstract
In this paper, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. We discovered a condition that preserves good quality in the MSU under different combinations of these three factors, providing a new useful criterion to help drive the process of dimension reduction.
Keywords
Cite
@article{arxiv.1709.08730,
title = {Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection},
author = {Gustavo Sosa-Cabrera and Miguel García-Torres and Santiago Gómez and Christian Schaerer and Federico Divina},
journal= {arXiv preprint arXiv:1709.08730},
year = {2023}
}