A fuzzy similarity-based approach for multi-label document classification

Shian Chi Tsai, Jung-Yi Jiang, Chun Der Wu, Shie Jue Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

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 decided to belong to every class whose score passes a threshold. To make the system adaptive, we provide a heuristic approach to find a score threshold automatically for each class. Experimental results show that our proposed method is more effective and efficient than other existing methods.

Original languageEnglish
Title of host publication2nd International Workshop on Computer Science and Engineering, WCSE 2009
Pages59-63
Number of pages5
DOIs
Publication statusPublished - 2009 Dec 1
Event2nd International Workshop on Computer Science and Engineering, WCSE 2009 - Qingdao, China
Duration: 2009 Oct 282009 Oct 30

Publication series

Name2nd International Workshop on Computer Science and Engineering, WCSE 2009
Volume2

Conference

Conference2nd International Workshop on Computer Science and Engineering, WCSE 2009
Country/TerritoryChina
CityQingdao
Period09-10-2809-10-30

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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