Coevolutionary Feature Selection Strategy for RBFNN Classifier

Author

Tian, Jin ; Li, Minqiang ; Chen, Fuzan

Author_Institution

Sch. of Manage., Tianjin Univ., Tianjin

Volume

3

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

131

Lastpage

135

Abstract

This paper presents a new hybrid learning algorithm based on cooperative coevolutionary algorithm (Co-CEA) for designing the radial basis function neural network (RBFNN) classifiers with an inductive feature selection. The hidden layer design and the feature selection correspond to two subpopulations. Collaborations among the two subpopulations are formed to obtain complete solutions. Experimental results illustrate that the proposed algorithm is able to achieve both good RBFNN structures and significant feature sets.

Keywords

evolutionary computation; learning (artificial intelligence); pattern classification; radial basis function networks; RBFNN classifier; coevolutionary feature selection strategy; cooperative coevolutionary algorithm; hidden layer design; hybrid learning algorithm; inductive feature selection; radial basis function neural network; Algorithm design and analysis; Collaboration; Conference management; Degradation; Design methodology; Encoding; Filters; Partitioning algorithms; Radial basis function networks; Utility programs; RBFNN; cooperative coevolutionary algorithms; feature selection; multiclass classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.436

Filename

4667116