Feature Selection by Using Heuristic Methods for Text Classification

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridsel, ilhami/0000-0003-0222-7017
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidsel, ilhami/ABD-7350-2020
dc.contributor.authorSel, Ilhami
dc.contributor.authorYeroglu, Celalettin
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:46:55Z
dc.date.available2024-08-04T20:46:55Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractFeature selection can be defined as the selection of the best subset to represent the data set in machine learning applications, in other words extraction of the unnecessary data that has no effect on the result. In classification problems efficiency and accuracy of the system can be increased when the dimension is reduced by feature selection. In this study, text classifying application is performed by using the data set of 20 News Group released in Reuters News Agent. The pre-processed news data were converted to vectors by using Doc2Vec method and the data set was created and classified by Naive Bayes method. Subsequently, a subset of the data set was formed by using heuristic methods that were inspired by nature (Whale and Gray Wolf Optimization Algorithms) and Chi-square method for feature selection. Then the reclassification was applied and the results were compared. While the success of the system with 600 features before the feature selection is 0.9214, the performance ratio of the 100 featured models created later is figured higher (0.94095 - 0.93833- 0.93619).en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci,Inonu Univ, Comp Sci Dept,Inonu Univ, Muhendisli Fakultesien_US
dc.identifier.scopus2-s2.0-85074896922en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/99048
dc.identifier.wosWOS:000591781100024en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 International Conference on Artificial Intelligence and Data Processing (Idap 2019)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNatural Language Processingen_US
dc.subjectDoc2Vecen_US
dc.subjectWhale Optimizationen_US
dc.subjectGrey Wolf Optimizationen_US
dc.subjectChi-Squareen_US
dc.titleFeature Selection by Using Heuristic Methods for Text Classificationen_US
dc.typeConference Objecten_US

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