Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection

Autor:	Sosa-Cabrera, Gustavo, García-Torres, Miguel, Gómez, Santiago, Schaerer, Christian, Divina, Federico
Rok vydání:	2017
Předmět:	Computer Science - Machine Learning Computer Science - Information Theory Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1709.08730 Zobrazit plný text záznamu View this record from Arxiv