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We propose an efficient approach, FSKNN, which employs fuzzy similarity measure (FSM) and k nearest neighbors (KNN), for multi-label text classification. One of the problems associated with KNN-like approaches is its demanding computational cost in finding the k nearest neighbors from all the training patterns. For FSKNN, FSM is used to group the training patterns into clusters. Then only the training...
Multi-label classification learning concerns the determination of categories in the situation where one pattern may belong to more than one category. In this paper we propose a mixture approach, named FSMLKNN, which combines Fuzzy Similarity Measure (FSM) and Multi-Label K-Nearest Neighbor (MLKNN) for multi-label document classification. One of the problems associated with KNN-like approaches is its...
Multi-label document classification concerns the determination of categories in the situation where one document may belong to more than one category. In this paper we propose a fuzzy similarity-based approach for multi-label document classification. For a test document, the scores of its relevance to the classes are calculated based on a modified fuzzy similarity measure. The test document is then...
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