Fusion of structure adaptive self-organizing maps using fuzzy integral

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 classifier 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 classifier including ID3, BP and naive 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.