DocumentCode
1684086
Title
Selectively ensembling neural classifiers
Author
Zhou, Zhi-Hua ; Wu, Jianxin ; Tang, Wei ; Chen, Zhao-Qian
Author_Institution
Nat. Lab. for Novel Software Technol., Nanjing Univ., China
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1411
Lastpage
1415
Abstract
Ensembling neural classifiers can significantly improve the generalization ability of classification systems. In this paper, GASEN, a genetic algorithm based selective ensemble method, that has been shown to be excellent in ensembling neural regressors, is applied to neural classifiers. Experiments on four large data sets show that this method can generate ensembles of neural classifiers with stronger generalization ability than those generated by Bagging, Adaboost, or Arc-x4
Keywords
classification; generalisation (artificial intelligence); genetic algorithms; neural nets; GASEN; classification; generalization; genetic algorithm; heuristics; neural classifiers; neural network ensemble; neural regressors; selective ensemble method; Bagging; Biomedical optical imaging; Character recognition; Face recognition; Genetic algorithms; Handwriting recognition; Image recognition; Laboratories; Neural networks; Optical character recognition software;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007723
Filename
1007723
Link To Document