Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/73093
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dc.titleA simple probability based term weighting scheme for automated text classification
dc.contributor.authorLiu, Y.
dc.contributor.authorLoh, H.T.
dc.date.accessioned2014-06-19T05:31:05Z
dc.date.available2014-06-19T05:31:05Z
dc.date.issued2007
dc.identifier.citationLiu, Y.,Loh, H.T. (2007). A simple probability based term weighting scheme for automated text classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4570 LNAI : 33-43. ScholarBank@NUS Repository.
dc.identifier.isbn9783540733225
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/73093
dc.description.abstractIn the automated text classification, tfidf is often considered as the default term weighting scheme and has been widely reported in literature. However, tfidf does not directly reflect terms' category membership. Inspired by the analysis of various feature selection methods, we propose a simple probability based term weighting scheme which directly utilizes two critical information ratios, i.e. relevance indicators. These relevance indicators are nicely supported by probability estimates which embody the category membership. Our experimental study based on two data sets, including Reuters-21578, demonstrates that the proposed probability based term weighting scheme outperforms tfidf significantly using Bayesian classifier and Support Vector Machines (SVM). © Springer-Verlag Berlin Heidelberg 2007.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume4570 LNAI
dc.description.page33-43
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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