A classifier based on the maximal fuzzy similarity in the generalized Lukasiewicz-structure

Author

Luukka, Pasi ; Saastamoinen, Kalle ; Könönen, Ville

Author_Institution

Lab. of Appl. Math., Lappeenranta Univ. of Technol., Finland

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

195

Lastpage

198

Abstract

The aim of this paper is to introduce improvements made to a classifier based on maximal fuzzy similarity. Improvements are based on the use of generalized Lukasiewicz-structure and weight optimization. The main benefits of the classifier are its computational efficiency and its strong mathematical background. It is based on many-valued logic and it provides semantic information about classification results. We show that if one chooses the power value in a right manner in the generalized Lukasiewicz-structure and the optimal weights for different feature, one can see significant enhancements in classification results

Keywords

fuzzy logic; genetic algorithms; multivalued logic; pattern classification; Lukasiewicz-structure; computational efficiency; fuzzy classifier; fuzzy logic; genetic algorithms; many-valued logic; maximal fuzzy similarity; pattern recognition; semantic information; weight optimization; Fuzzy set theory; Genetic algorithms; Information processing; Intelligent networks; Laboratories; Lattices; Mathematics; Neural networks; Pattern recognition; Peak to average power ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1007281

Filename

1007281