Determine Fuzzy Measures in Multiple Classifiers Fusion Model

Author

Chen, Jun-Fen ; Guo, Mei-fang ; Feng, Hui-min ; Zhao, Shi-Xin

Author_Institution

Machine Learning Center, Hebei Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2204

Lastpage

2207

Abstract

In finite set, Choquet fuzzy integral with respect to fuzzy measures can be transferred into linear combination of product, based on this fact we can choose standard optimization technical to determine fuzzy measures. This paper present linear programming and quadratic programming to determine fuzzy measures, the experiments demonstrate that classification accuracy of fuzzy integral with respect to fuzzy measure is better than the classification accuracies of majority voting and weighted average

Keywords

fuzzy set theory; integral equations; learning (artificial intelligence); linear programming; pattern classification; quadratic programming; sensor fusion; Choquet fuzzy integral; finite set; fuzzy measure; linear programming; majority voting method; multiple classifier fusion model; optimization; quadratic programming; weighted averaging method; Computer science; Cybernetics; Fuzzy sets; Linear programming; Machine learning; Mathematical model; Mathematics; Physics; Power measurement; Quadratic programming; Voting; Fuzzy integral; Fuzzy measure; Majority voting; Multiple classifiers; Weighted average;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258621

Filename

4028429