A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

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• 作者：Douglas Rodrigues ; Lu铆s A.M. Pereira ; Rodrigo Y.M. Nakamura ; Kelton A.P. Costa ; Xin-She Yang ; Andr茅 N. Souza ; Jo茫o Paulo Papa
• 关键词：Dimensionality reduction ; Swarm intelligence ; Bat Algorithm ; Optimum-Path Forest
• 刊名：Expert Systems with Applications
• 出版年：April, 2014
• 年：2014
• 卷：41
• 期：5
• 页码：2250-2258
• 全文大小：1073 K

文摘

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness.