Fusion of Structure Adaptive Self-organizing Maps Using Fuzzy Integral

Kyung Joong Kim, Sung Bae Cho

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Recently, many researchers attempt to develop an effective SOM-based pattern recognizer for high performance classification. Structure adaptive self-organizing map (SASOM) is a variant of SOM that is useful to pattern recognition and visualization. Fusion of classifiers can overcome the limitation of a single classifier by complementing each other. Fuzzy integral is a combination scheme that uses subjectively defined relevance of classifiers. In this paper, fusion of SASOM's using fuzzy integral is proposed for web mining problem. User profile represents different aspects of user's characteristics and needs an ensemble of classifiers that estimate user's preference using web content labeled by user as "like" or "dislike." The proposed method estimates the user profile using subsets of important features extracted from user-rated web documents. Using UCI Syskill & Webert data, the method is tested and compared with other classifiers including ID3, BP and naïve Bayes classifier. Experimental results show that the fusion of SASOM's using fuzzy integral can perform better than not only previous studies but also majority voting of SASOM's.

Original languageEnglish
Pages28-33
Number of pages6
Publication statusPublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 2003 Jul 202003 Jul 24

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period03/7/2003/7/24

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Kim, K. J., & Cho, S. B. (2003). Fusion of Structure Adaptive Self-organizing Maps Using Fuzzy Integral. 28-33. Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.