Multiple feature-classifier combination in automated text classification

Lazaro S.P. Busagala; Wataru Ohyama; Tetsushi Wakabayashi; Fumitaka Kimura

doi:10.1109/DAS.2012.56

Multiple feature-classifier combination in automated text classification

Lazaro S.P. Busagala, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

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

3 Citations (Scopus)

Abstract

Automatic text classification (ATC) is important in applications such as indexing and organizing electronic documents in databases leading to enhancement of information access and retrieval. We propose a method which employs various types of feature sets and learning algorithms to improve classification effectiveness. Unlike the conventional methods of multi-classifier combination, the proposed method considers the contributions of various types of feature sets and classifiers. It can therefore be known as multiple feature-classifier combination (MFC) method. In this paper we present empirical evaluation of MFC using two benchmarks of text collections to determine its effectiveness. Empirical evaluation show that MFC consistently outperformed all compared methods.

Original language	English
Title of host publication	Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012
Pages	43-47
Number of pages	5
DOIs	https://doi.org/10.1109/DAS.2012.56
Publication status	Published - 2012
Externally published	Yes
Event	10th IAPR International Workshop on Document Analysis Systems, DAS 2012 - Gold Coast, QLD, Australia Duration: Mar 27 2012 → Mar 29 2012

Publication series

Name	Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012

Other

Other	10th IAPR International Workshop on Document Analysis Systems, DAS 2012
Country/Territory	Australia
City	Gold Coast, QLD
Period	3/27/12 → 3/29/12

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

Access to Document

10.1109/DAS.2012.56

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Busagala, L. S. P., Ohyama, W., Wakabayashi, T., & Kimura, F. (2012). Multiple feature-classifier combination in automated text classification. In Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012 (pp. 43-47). Article 6195332 (Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012). https://doi.org/10.1109/DAS.2012.56

Multiple feature-classifier combination in automated text classification. / Busagala, Lazaro S.P.; Ohyama, Wataru; Wakabayashi, Tetsushi et al.
Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012. 2012. p. 43-47 6195332 (Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012).

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

Busagala, LSP, Ohyama, W, Wakabayashi, T & Kimura, F 2012, Multiple feature-classifier combination in automated text classification. in Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012., 6195332, Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012, pp. 43-47, 10th IAPR International Workshop on Document Analysis Systems, DAS 2012, Gold Coast, QLD, Australia, 3/27/12. https://doi.org/10.1109/DAS.2012.56

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author = "Busagala, {Lazaro S.P.} and Wataru Ohyama and Tetsushi Wakabayashi and Fumitaka Kimura",

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AB - Automatic text classification (ATC) is important in applications such as indexing and organizing electronic documents in databases leading to enhancement of information access and retrieval. We propose a method which employs various types of feature sets and learning algorithms to improve classification effectiveness. Unlike the conventional methods of multi-classifier combination, the proposed method considers the contributions of various types of feature sets and classifiers. It can therefore be known as multiple feature-classifier combination (MFC) method. In this paper we present empirical evaluation of MFC using two benchmarks of text collections to determine its effectiveness. Empirical evaluation show that MFC consistently outperformed all compared methods.

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Multiple feature-classifier combination in automated text classification

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