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Title:An Evaluation of Text Classification Methods for Literary Study
Author(s):Yu, Bei
Doctoral Committee Chair(s):Linda Smith
Department / Program:Library and Information Science
Discipline:Library and Information Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Literature, American
Abstract:Some of our conclusions are consistent with what are obtained in topic classification, such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this area---SVM and naive Bayes select top features in different frequency ranges; stemming might harm feature selection methods. These experiment results provide new insights to the relation between classification methods, feature engineering options and non-topic document properties. They also provide guidance for classification method selection in literary text classification applications.
Issue Date:2006
Description:116 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Other Identifier(s):(MiAaPQ)AAI3250350
Date Available in IDEALS:2015-09-25
Date Deposited:2006

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