Fuzzy integration of structure adaptive SOMs for web content mining

Kyung Joong Kim, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Since exponentially growing web contains giga-bytes of web documents, users are faced with difficulty to find an appropriate web site. Using profile, information retrieval system can personalize browsing of the web by recommending suitable web sites. User's evaluation on web content can be used to predict users' preference on web sites and construct profiles automatically. User profile represents different aspects of user's characteristics, thereby we need an ensemble of classifiers that estimate user's preference using web content labeled by user as "like" or "dislike." Fuzzy integral is a combination scheme that uses subjectively defined relevance of classifiers and structure adaptive self-organizing map (SASOM) is a variant of SOM that is useful to pattern recognition and visualization. In this paper, fuzzy integral-based ensemble of SASOMs trained independently is used to estimate user profile and tested on UCI Syskill & Webert data. Experimental results show that the proposed method can perform better than not only previous naïve Bayes classifier but also majority voting of SASOMs.

Original languageEnglish
Pages (from-to)43-60
Number of pages18
JournalFuzzy Sets and Systems
Issue number1
Publication statusPublished - 2004 Nov 16

Bibliographical note

Funding Information:
This work was supported by Biometrics Engineering Research Center, and Brain Science and Engineering Research Program sponsored by Korean Ministry of Science and Technology.

All Science Journal Classification (ASJC) codes

  • Logic
  • Artificial Intelligence


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