FMLKNN: A fuzzy membership function based K-Nearest Neighbor approach for multi-label classification

Jung-Yi Jiang, Shie Jue Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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 fuzzy membership function based K-Nearest Neighbor algorithm, named FMLKNN, for multi-label classification. For an unseen pattern, the scores of its relevance to the classes are calculated based on a given membership function. The unseen pattern is then decided to belong to every class to which the pattern's membership degree of "belong to the class" is higher than the degree of "not belong to the class". The membership function used can be learned from applying the Multi-Label K-Nearest Neighbor algorithm (MLKNN). Experimental results show that our proposed method achieves better performance than others. ICIC International

Original languageEnglish
Pages (from-to)1069-1075
Number of pages7
JournalICIC Express Letters
Volume5
Issue number4 A
Publication statusPublished - 2011 Apr 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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