A new feature selection method on classification of medical datasets: Kernel F-score feature selection

dc.contributor.authorPolat, Kemal
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:37:44Z
dc.date.available2020-03-26T17:37:44Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg-Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection. (C) 2009 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2009.01.041en_US
dc.identifier.endpage10373en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue7en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10367en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2009.01.041
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23216
dc.identifier.volume36en_US
dc.identifier.wosWOS:000266851000019en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectFeature selectionen_US
dc.subjectKernel F-score feature selectionen_US
dc.subjectLeast Square Support Vector Machine (LS-SVM)en_US
dc.subjectLevenberg-Marquardt Artificial Neural Networken_US
dc.subjectHeart disease dataseten_US
dc.subjectSPECT images dataseten_US
dc.subjectEscherichia coli Promoter Gene Sequence dataseten_US
dc.titleA new feature selection method on classification of medical datasets: Kernel F-score feature selectionen_US
dc.typeArticleen_US

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