A novel filter feature selection method for text classification: Extensive Feature Selector

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dc.authorid0000-0002-4057-934Xen_US
dc.authorscopusid55293773800en_US
dc.authorwosidDYK-8713-2022en_US
dc.contributor.authorParlak, Bekir
dc.contributor.authorUysal, Alper Kürşat
dc.date.accessioned2022-12-31T09:31:04Z
dc.date.available2022-12-31T09:31:04Z
dc.date.issuedEarly Accessen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAs the huge dimensionality of textual data restrains the classification accuracy, it is essential to apply feature selection (FS) methods as dimension reduction step in text classification (TC) domain. Most of the FS methods for TC contain several number of probabilities. In this study, we proposed a new FS method named as Extensive Feature Selector (EFS), which benefits from corpus-based and classbased probabilities in its calculations. The performance of EFS is compared with nine well-known FS methods, namely, Chi-Squared (CHI2), Class Discriminating Measure (CDM), Discriminative Power Measure (DPM), Odds Ratio (OR), Distinguishing Feature Selector (DFS), Comprehensively Measure Feature Selection (CMFS), Discriminative Feature Selection (DFSS), Normalised Difference Measure (NDM) and Max–Min Ratio (MMR) using Multinomial Naive Bayes (MNB), Support-Vector Machines (SVMs) and k-Nearest Neighbour (KNN) classifiers on four benchmark data sets. These data sets are Reuters-21578, 20-Newsgroup, Mini 20-Newsgroup and Polarity. The experiments were carried out for six different feature sizes which are 10, 30, 50, 100, 300 and 500. Experimental results show that the performance of EFS method is more successful than the other nine methods in most cases according to microF1 and macro-F1 scores.en_US
dc.identifier.citationParlak, B., & Uysal, A. K. (2021). A novel filter feature selection method for text classification: Extensive feature selector. Journal of Information Science, doi:10.1177/0165551521991037en_US
dc.identifier.doi10.1177/0165551521991037
dc.identifier.endpage20en_US
dc.identifier.issn0165-5515
dc.identifier.issn1741-6485
dc.identifier.issuePublished Online : 04.2021en_US
dc.identifier.scopus2-s2.0-85104287630
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1177/0165551521991037
dc.identifier.urihttps://hdl.handle.net/20.500.12428/3764
dc.identifier.volumeEarly Accessen_US
dc.identifier.wosWOS:000641912500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUysal, Alper Kürşat
dc.language.isoen
dc.publisherSAGE Publicationsen_US
dc.relation.ispartofJournal of Information Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDimension Reductionen_US
dc.subjectFeature Selectionen_US
dc.subjectText Classificationen_US
dc.titleA novel filter feature selection method for text classification: Extensive Feature Selector
dc.typeArticle

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