Paper Info
Title
A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest
Abstract
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.
Year
DOI
Venue
2014
10.1016/j.eswa.2013.09.023
Expert Syst. Appl.
Keywords
Field
DocType
feature selection,classification model,wrapper approach,optimum-path forest,wrapper feature selection approach,bat algorithm,dimensionality problem,classification effectiveness,reduced feature set,opf accuracy,feature selection technique,compact set,swarm intelligence,dimensionality reduction
Data mining,Dimensionality reduction,Bat algorithm,Feature selection,Computer science,Swarm intelligence,Artificial intelligence,Classifier (linguistics),Binary number,Pattern recognition,Fitness function,Curse of dimensionality,Machine learning
Journal
Volume
Issue
ISSN
41
5
0957-4174
Citations 
PageRank 
References 
58
2.05
21
Authors
7
Name
Order
Citations
PageRank
Douglas Rodrigues1765.12
Luís A. M. Pereira21298.87
Rodrigo Y. M. Nakamura31206.09
Kelton A. P. Costa41258.66
Xin-She Yang55433241.09
André N. Souza61269.61
João Paulo Papa727844.60