A Evolving Fuzzy Classification System

Author

Yang, Ai-Min ; Zhou, Yong-Mei ; Tang, Min ; Liu, Ping

Author_Institution

Dept. of Comput. Sci., Hunan Univ. of Technol., ZhuZhou

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1615

Lastpage

1620

Abstract

In this paper, an evolving fuzzy classifier system is introduced. First, the basic characters and structure frames of this system is introduced. Then, the dynamic clustering algorithms which can dynamically cluster the input training patterns is presented. For each cluster, a fuzzy rule with an ellipsoidal region around a cluster center is defined. The strategy of tuning fuzzy rules is that the slopes of the membership functions are tuned successively until there is no improvement in the recognition rate of the training patterns. The tuning method and the policy of inserting rules and aggregating rules are discussed. This system is evaluated with the Fisher iris data and pen-based recognition of handwritten digits data

Keywords

fuzzy set theory; knowledge based systems; pattern clustering; Fisher iris data; dynamic clustering algorithm; fuzzy classification system; handwritten digits data; pen-based recognition; tuning fuzzy rules; Clustering algorithms; Computer science; Cybernetics; Electronic mail; Fuzzy neural networks; Fuzzy systems; Handwriting recognition; Heuristic algorithms; Iris; Machine learning; Neural networks; Pattern recognition; Dynamic Clustering; Ellipsoidal Regions; Fuzzy Classification;

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.2006.258839

Filename

4028323