Feature selection optimization with filtering and wrapper methods: two disease classification cases

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dc.WoS.categoriesComputer Science, Artificial IntelligenceEngineering, Electrical & Electronicen_US
dc.authorid0000-0002-5330-5620en_US
dc.contributor.authorAtik, Serhat
dc.contributor.authorDalyan, Tuğba
dc.date.accessioned2024-04-03T07:21:23Z
dc.date.available2024-04-03T07:21:23Z
dc.date.issued2023
dc.description.abstractDiscarding the less informative and redundant features helps to reduce the time required to train a learning algorithm and the amount of storage required, improving the learning accuracy as well as the quality of results. In this study, we present different feature selection approaches to address the problem of disease classification based on the Parkinson and Cardiac Arrhythmia datasets. For this purpose, first we utilize three filtering algorithms including the Pearson correlation coefficient, Spearman correlation coefficient, and relief. Second, metaheuristic algorithms are compared to find the most informative subset of the features to obtain better classification accuracy. As a final method, a hybrid model involving filtering algorithms is applied to the datasets to eliminate half of the features, and then a metaheuristic algorithm based on a proposed genetic algorithm is applied to the rest of the datasets. With all three methods, we use three classification algorithms: support vector machine, K-nearest neighbor, and random forest. The results show that the best scores are obtained from the metaheuristic algorithm based on the proposed genetic algorithm for both datasets. This comparative study contributes to the literature by increasing the accuracy of classification for both datasets and presenting a hybrid model with filtering and a metaheuristic algorithm.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.55730/1300-0632.4050
dc.identifier.issn1303-6203
dc.identifier.issn1300-0632
dc.identifier.scopus2-s2.0-85180007609en_US
dc.identifier.trdizinid1221029en_US
dc.identifier.urihttps://hdl.handle.net/11411/5237
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4050
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1221029en_US
dc.identifier.wosWOS:001115009000005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.issue7en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors2en_US
dc.pages1329-1342en_US
dc.publisherScientific and Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature selectionen_US
dc.subjectoptimization algorithmsen_US
dc.subjectmetaheuristic algorithmsen_US
dc.subjectgenetic algorithmsen_US
dc.subjectfiltering methodsen_US
dc.titleFeature selection optimization with filtering and wrapper methods: two disease classification cases
dc.typeArticle
dc.volume31en_US

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