Rapid and brief communication: Eliminating redundancy and irrelevance using a new MLP-based feature selection method

Authors:
E. Gasca;J. S. Sánchez;R. Alonso
Affiliations:
Lab. Reconocimiento de Patrones, Instituto Tecnológico de Toluca, Av. Tecnológico s/n, 52140 Metepec, Edomex, México;Dept. Llenguatges i Sistemes Informítics, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain;Lab. Reconocimiento de Patrones, Instituto Tecnológico de Toluca, Av. Tecnológico s/n, 52140 Metepec, Edomex, México
Venue:
Pattern Recognition
Year:
2006

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Abstract

This paper presents a novel feature selection method based on the use of a multilayer perceptron (MLP). The algorithm identifies a subset of relevant, non-redundant attributes for supervised pattern classification by estimating the relative contribution of the input units (those representing the attributes) to the output neurons (those corresponding to the problem classes). The experimental results suggest that the proposed method works well on a variety of real-world domains.