A Symmetric Term Weighting Scheme for Text Categorization Based on Term Occurrence Probabilities

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dc.contributor.authorErenel, Zafer
dc.contributor.authorAltincay, Hakan
dc.contributor.authorVaroglu, Ekrem
dc.date.accessioned2026-02-06T18:28:22Z
dc.date.issued2010
dc.departmentDoğu Akdeniz Üniversitesi
dc.description5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control -- SEP 02-04, 2009 -- Famagusta, CYPRUS
dc.description.abstractTerm weighting schemes used in text categorization can be considered as functions of term occurence probabilities in positive and negative classes. In this paper, widely used weighting schemes are firstly evaluated from this perspective. Then, a novel feature weighting scheme based on term occurrence probabilities is proposed. Experiments conducted using SVM classifier on the Reuters-21578 ModApte Top10 dataset shows that the proposed method outperforms other well known measures such as CHI, IG, OR and RF in terms of macro-F-1 and micro-F-1 scores.
dc.identifier.endpage218
dc.identifier.isbn978-1-4244-3429-9
dc.identifier.scopus2-s2.0-77950498503
dc.identifier.scopusqualityN/A
dc.identifier.startpage215
dc.identifier.urihttps://hdl.handle.net/11129/10874
dc.identifier.wosWOS:000287219100053
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2009 Fifth International Conference on Soft Computing, Computing With Words and Perceptions in System Analysis, Decision and Control
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260204
dc.titleA Symmetric Term Weighting Scheme for Text Categorization Based on Term Occurrence Probabilities
dc.typeConference Object

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