Improving Simplified Fuzzy ARTMAP Performance Using Genetic Algorithm for Brain Fingerprint Classification

Author

Palaniappan, Ramaswamy ; Krishnan, Shankar M. ; Eswaran, Chikkanan

Author_Institution

Univ. of Essex, Colchester

fYear

2006

fDate

20-23 Dec. 2006

Firstpage

327

Lastpage

330

Abstract

A genetic algorithm is proposed for ordering the input patterns during training for simplified fuzzy ARTMAP (SFA) classifier to improve the individual identification classification performance using brain fingerprints. The results indicate improved classification performance as compared to the existing methods for pattern ordering, namely voting strategy and min-max. As the ordering method is general, it could be used with any dataset to obtain improved classification performance when SFA is used.

Keywords

brain; fingerprint identification; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); minimax techniques; neurophysiology; pattern classification; analog multidimensional map; brain fingerprint identification classification; genetic algorithm; incremental supervised learning; min-max; neural network architecture; pattern ordering; simplified fuzzy ARTMAP; voting strategy; Biological cells; Biomedical engineering; Brain modeling; Computer science; Fingerprint recognition; Genetic algorithms; Information technology; Signal processing; Testing; Voting;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computing and Communications, 2006. ADCOM 2006. International Conference on

Conference_Location

Surathkal

Print_ISBN

1-4244-0716-8

Electronic_ISBN

1-4244-0716-8

Type

conf

DOI

10.1109/ADCOM.2006.4289909

Filename

4289909