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dc.contributor.authorZhu, Dengya
dc.contributor.authorWong, K.
dc.contributor.editorChu Kiong Loo
dc.contributor.editorKeem Siah Yap
dc.contributor.editorKok Wai Wong
dc.contributor.editorAndrew Teoh
dc.contributor.editorKaizhu Huang
dc.identifier.citationZhu, D. and Wong, K. 2014. Text Categorization Using an Automatically Generated Labelled Dataset: An Evaluation Study, in Loo, C.K. and Yap, K.S. and Wong, K.W. and Teoh, A. and Huang, K. (ed), Proceedings of 21st International Conference on Neural Information Processing: The Next Renaissance of the Neural Information Processing (Part 1), Nov 3-6 2014, pp. 479-486. Sarawak, Malaysia: University of Malaya.

Naïve Bayes(NB), kNN and Adaboost are three commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, labeling and corpus bias are two concerns in text categorization. This paper focuses on evaluating the three commonly used text classifiers by using an automatically generated text document set which is labelled by a group of experts to alleviate subjectiveness of labelling, and at the same time to examine how the performance of the algorithms is influenced by feature selection algorithms and the number of features selected.

dc.publisherSpringer International Publishing
dc.subjectfeature selection
dc.subjecttext classifiers
dc.subjectText categorization
dc.titleText Categorization Using an Automatically Generated Labelled Dataset: An Evaluation Study
dc.typeConference Paper
dcterms.source.titleNeural Information Processing
dcterms.source.seriesNeural Information Processing
dcterms.source.conferenceICONIP 2014
dcterms.source.conference-start-dateNov 3 2014
dcterms.source.conferencelocationKuching, Malaysia
curtin.departmentSchool of Information Systems
curtin.accessStatusFulltext not available

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