Exploring constructive algorithms with stopping criteria to produce accurate and diverse individual neural networks in an ensemble

Author

Islam, Md Monirul ; Shahjahan, Md ; Murase, K.

Author_Institution

Dept. of Human & Artificial Intelligence Syst., Fukui Univ., Japan

Volume

3

fYear

2001

fDate

2001

Firstpage

1526

Abstract

Explores the use of constructive algorithms with stopping criteria to design accurate and diverse individual neural networks (NNs) in an ensemble. Based on constructive algorithms and stopping criteria, a constructive neural network ensemble (CNNE) method to design accurate and diverse individual NNs in an ensemble is proposed. The CNNE is applied to three benchmarked problems in NNs. They are cancer, diabetes and heart disease problems. The experimental results demonstrate the effectiveness of CNNE

Keywords

cancer; learning (artificial intelligence); neural nets; patient diagnosis; cancer; constructive algorithms; constructive neural network ensemble method; diabetes; ensemble; generalization ability; heart disease; individual neural networks; stopping criteria; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Cancer; Cardiac disease; Design methodology; Diabetes; Humans; Neural networks; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2001 IEEE International Conference on

Conference_Location

Tucson, AZ

ISSN

1062-922X

Print_ISBN

0-7803-7087-2

Type

conf

DOI

10.1109/ICSMC.2001.973500

Filename

973500