Multiple feature-classifier combination in automated text classification

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

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

2 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 languageEnglish
Title of host publicationProceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012
Pages43-47
Number of pages5
DOIs
Publication statusPublished - May 24 2012
Event10th IAPR International Workshop on Document Analysis Systems, DAS 2012 - Gold Coast, QLD, Australia
Duration: Mar 27 2012Mar 29 2012

Publication series

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

Other

Other10th IAPR International Workshop on Document Analysis Systems, DAS 2012
CountryAustralia
CityGold Coast, QLD
Period3/27/123/29/12

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Busagala, L. S. P., Oyama, 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). [6195332] (Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012). https://doi.org/10.1109/DAS.2012.56