title: Relevance popularity: A term event model based feature selection scheme for text classification.
abstract: Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.
authors.affiliation: Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.|Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Changchun, 130024, China.|Institute of Computational Biology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Guozhong
authors.identifier: http://orcid.org/0000-0002-1953-2766
authors.initials: G
authors.lastname: Feng
authors.affiliation: School of Statistics, Capital University of Economics and Business, Beijing, 100070, China.
authors.forename: Baiguo
authors.initials: B
authors.lastname: An
authors.affiliation: Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Fengqin
authors.initials: F
authors.lastname: Yang
authors.affiliation: Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.|Institute of Computational Biology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Han
authors.initials: H
authors.lastname: Wang
authors.affiliation: Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Libiao
authors.initials: L
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D002965:Classification; D007802:Language; D008962:Models, Theoretical; D009323:Natural Language Processing; D060388:Support Vector Machine