ELM-based Multiple Classifier Systems

Author

Wang, Dianhui

Author_Institution

Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Melbourne, Vic.

fYear

2006

fDate

5-8 Dec. 2006

Firstpage

1

Lastpage

5

Abstract

With random weights between the inputs and the hidden units of three-layer feed-forward neural networks (namely extreme learning machine (ELM)), some favorable performance may be achieved for pattern classifications in terms of efficiency and effectiveness. This paper aims to investigate properties of ELM-based multiple classifier systems (MCS). A protein database with ten classes of super-families is employed in this study. Our results indicate that (1) integration of the base ELM classifiers with better learning performance may result in a MCS with better generalization power; (2) smaller size of weights in ELM classifiers does not imply a better generalization capability; and (3) under/over-fitting phenomena occurs for classification as inappropriate network architectures are used

Keywords

biology computing; feedforward neural nets; generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; proteins; extreme learning machine; feedforward neural networks; generalization; learning performance; multiple classifier systems; network architecture; pattern classification; protein database; Computer networks; Computer science; Databases; Electronic mail; Feedforward neural networks; Feedforward systems; Machine learning; Neural networks; Neurons; Proteins;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on

Conference_Location

Singapore

Print_ISBN

1-4244-0341-3

Electronic_ISBN

1-4214-042-1

Type

conf

DOI

10.1109/ICARCV.2006.345466

Filename

4150395