A NEW FEATURE SELECTION METHOD FOR TEXT CATEGORIZATION BASED ON INFORMATION GAIN AND PARTICLE SWARM OPTIMIZATION

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dc.contributor.authorYigit, Ferruh
dc.contributor.authorBaykan, Omer Kaan
dc.date.accessioned2020-03-26T18:49:13Z
dc.date.available2020-03-26T18:49:13Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description3rd IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) -- NOV 27-29, 2014 -- PEOPLES R CHINAen_US
dc.description.abstractRapid increases of the documents which are created in digital media necessitate analyze and classify of these documents automatically. Feature extraction, feature selection and classifier selection in the analysis of documents and classification affects performance. In text document categorization, it is a fundamental problem that the numbers of extracted features are a lot of. In this study, by using a new feature selection method based on IG (information gain) and PSO (particle swarm optimization) algorithms, text categorization process performed. Reuters 21.578 and Classic3 corpus were used in the experiments. The roots of the words in the texts of corpus were taken as the features. Feature selection and categorization processes performed with k-Nearest Neighbors algorithm (K-NN) and Naive Bayes classifiers by using IG and PSO algorithms. Proposed system performance was evaluated by using CA (Classification Accuracy), Precision, Recall and F-measure criteria.en_US
dc.description.sponsorshipIEEE, AAI, SHENZHEN UNIV, HONG KONG POLYTECHN UNIV, DEPT IND & SYST ENGN, IEEE Beijing Sect, Chinese Assoc Artificial Intelligence, Beijing Univ Posts & Telecommunicat, Shenzhen Univ, Inst Engn & Technol, Shenzhen Bur Nanshan Dist Sci & Technol Innovaten_US
dc.description.sponsorshipScientific Research Project of SeIcuk University [13701497]en_US
dc.description.sponsorshipThis work is supported by the Scientific Research Project of SeIcuk University (No: 13701497).en_US
dc.identifier.endpage529en_US
dc.identifier.isbn978-1-4799-4719-5
dc.identifier.issn2376-5933en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage523en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30555
dc.identifier.wosWOS:000392727800100en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2014 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (CCIS)en_US
dc.relation.ispartofseriesInternational Conference on Cloud Computing and Intelligence Systems
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectText categorizationen_US
dc.subjectfeature selectionen_US
dc.subjectparticle swarm optimizationen_US
dc.titleA NEW FEATURE SELECTION METHOD FOR TEXT CATEGORIZATION BASED ON INFORMATION GAIN AND PARTICLE SWARM OPTIMIZATIONen_US
dc.typeConference Objecten_US

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