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Evaluating a new feature selection strategy based on mutual information

Grant number: 13/17320-8
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): November 01, 2013
Effective date (End): January 31, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Maria Carolina Monard
Grantee:Laís Pessine Do Carmo
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Feature Selection plays an important role in machine learning, as irrelevant and/or redundant features may reduce the quality and comprehensibility of the hypotheses induced by supervised learning algorithms. This work proposes the evaluation of a new feature selection strategy using mutual information, which will be estimated using a new method, recently proposed, related to the estimation of the ratio of two probability densities from a given collection of data. The algorithms developed using this new method to estimate mutual information will be experimentally evaluated, as well as compared with state-of-the-art feature selection algorithms