Support vector machines with evolutionary interval neural networks for granular feature transformation in making effective biomedical data classification

Author

Jin, Bo ; Zhang, Yan-Qing ; Hu, Xiaohua Tony

Author_Institution

Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA

Volume

1

fYear

2005

fDate

25-27 July 2005

Firstpage

163

Abstract

In this paper, we use new evolutionary interval neural networks to do granular feature transformation based on granular computing, neural computing and evolutionary computation to alleviate kernel´s burden in support vector machines (SVMs) and help SVMs learn knowledge effectively. Simulation results for three different medical data sets show that SVMs using the evolutionary interval neural networks are more effective than the traditional SVMs in terms of testing accuracy.

Keywords

evolutionary computation; medical information systems; neural nets; support vector machines; biomedical data classification; evolutionary computation; evolutionary interval neural network; granular computing; granular feature transformation; neural computing; support vector machine; Bioinformatics; Biomedical computing; Computational modeling; Computer networks; Evolutionary computation; Kernel; Medical simulation; Neural networks; Support vector machine classification; Support vector machines; Support Vector Machines; bioinformatics; classification; genetic algorithms; granular computing; granular feature transformation; interval neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2005 IEEE International Conference on

Print_ISBN

0-7803-9017-2

Type

conf

DOI

10.1109/GRC.2005.1547258

Filename

1547258