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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578680
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dc.contributor.authorYap Bee Wah (UITM)
dc.contributor.authorNurain Ibrahim (UITM)
dc.contributor.authorHamzah Abdul Hamid (UITM)
dc.contributor.authorShuzlina Abdul-Rahman (UITM)
dc.contributor.authorSimon Fong
dc.date.accessioned2023-11-06T03:05:41Z-
dc.date.available2023-11-06T03:05:41Z-
dc.date.issued2018-01
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116123
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578680-
dc.descriptionFeature selection has been widely applied in many areas such as classification of spam emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray analysis. Classification involves building predictive models to predict the target variable based on several input variables (features). This study compares filter and wrapper feature selection methods to maximise the classifier accuracy. The logistic regression was used as a classifier while the performance of the feature selection methods was based on the classification accuracy, Akaike information criteria (AIC), Bayesian information criteria (BIC), Area Under Receiver operator curve (AUC), as well as sensitivity and specificity of the classifier. The simulation study involves generating data for continuous features and one binary dependent variable for different sample sizes. The filter methods used are correlation based feature selection and information gain, while the wrapper methods are sequential forward and sequential backward elimination. The simulation was carried out using R, an open-source programming language. Simulation results showed that the wrapper method (sequential forward selection and sequential backward elimination) methods were better than the filter method in selecting the correct features.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-26-1-1
dc.rightsUKM
dc.subjectFeature selection methods
dc.subjectFilter method
dc.subjectLogistic regression
dc.subjectSimulation
dc.subjectWrapper method
dc.titleFeature selection methods: case of filter and wrapper approaches for maximising classification accuracy
dc.typeJournal Article
dc.format.volume26
dc.format.pages329-340
dc.format.issue1
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