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arXiv · 1709.08730

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

Gustavo Sosa-Cabrera·Miguel García-Torres·Santiago Gómez·Christian Schaerer·Federico Divina
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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.

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Gustavo Sosa-Cabrera, Miguel García-Torres, Santiago Gómez, Christian Schaerer, Federico Divina. 2017-09-25. Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection. https://arxiv.org/abs/1709.08730

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