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

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

doi:10.1109/WCSE.2009.766

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

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

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2nd International Workshop on Computer Science and Engineering, WCSE 2009
Pages	59-63
Number of pages	5
DOIs	https://doi.org/10.1109/WCSE.2009.766
Publication status	Published - 2009
Event	2nd International Workshop on Computer Science and Engineering, WCSE 2009 - Qingdao, China Duration: 2009 Oct 28 → 2009 Oct 30

Publication series

Name	2nd International Workshop on Computer Science and Engineering, WCSE 2009
Volume	2

Conference

Conference	2nd International Workshop on Computer Science and Engineering, WCSE 2009
Country/Territory	China
City	Qingdao
Period	09-10-28 → 09-10-30

All Science Journal Classification (ASJC) codes

General Computer Science

Access to Document

10.1109/WCSE.2009.766

Cite this

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title = "A fuzzy similarity-based approach for multi-label document classification",

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.",

author = "Tsai, {Shian Chi} and Jiang, {Jung Yi} and Wu, {Chun Der} and Lee, {Shie Jue}",

year = "2009",

doi = "10.1109/WCSE.2009.766",

language = "English",

isbn = "9780769538815",

series = "2nd International Workshop on Computer Science and Engineering, WCSE 2009",

pages = "59--63",

booktitle = "2nd International Workshop on Computer Science and Engineering, WCSE 2009",

note = "2nd International Workshop on Computer Science and Engineering, WCSE 2009 ; Conference date: 28-10-2009 Through 30-10-2009",

}

Tsai, SC, Jiang, JY, Wu, CD & Lee, SJ 2009, A fuzzy similarity-based approach for multi-label document classification. in 2nd International Workshop on Computer Science and Engineering, WCSE 2009., 5403378, 2nd International Workshop on Computer Science and Engineering, WCSE 2009, vol. 2, pp. 59-63, 2nd International Workshop on Computer Science and Engineering, WCSE 2009, Qingdao, China, 09-10-28. https://doi.org/10.1109/WCSE.2009.766

A fuzzy similarity-based approach for multi-label document classification. / Tsai, Shian Chi; Jiang, Jung Yi; Wu, Chun Der et al.
2nd International Workshop on Computer Science and Engineering, WCSE 2009. 2009. p. 59-63 5403378 (2nd International Workshop on Computer Science and Engineering, WCSE 2009; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A fuzzy similarity-based approach for multi-label document classification

AU - Tsai, Shian Chi

AU - Jiang, Jung Yi

AU - Wu, Chun Der

AU - Lee, Shie Jue

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - 2nd International Workshop on Computer Science and Engineering, WCSE 2009

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

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