Feature Selection using FFS and PCA in biomedical data classification with AdaBoost-SVM

dc.contributor.authorCeylan, Rahime
dc.contributor.authorBarstugan, Mucahid
dc.date.accessioned2020-03-26T19:45:21Z
dc.date.available2020-03-26T19:45:21Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Elektirik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractRecently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This process includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used, and the performance of the feature selection algorithms was tested by AdaBoost-SVM classifier. Following it, advantages and disadvantages of these algorithms were evaluated. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets were taken from UCI database and Electrocardiogram (ECG) signals were taken from Physionet ECG Database, and were used to test the proposed hybrid structure. The used two hybrid structures and other studies in the literature were compared with our findings. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classificationen_US
dc.identifier.citationCeylan, R., Barstugan, M. (2018). Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM. International Journal of Intelligent Systems and Applications in Engineering, 6(1), 33-39.
dc.identifier.endpage39en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue1en_US
dc.identifier.startpage33en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TWpZNE16TXlNZz09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36026
dc.identifier.volume6en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorCeylan, Rahime
dc.institutionauthorBarstugan, Mucahid
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYapay Zekaen_US
dc.subjectAdaBoost, Biomedical Data Classification
dc.subjectClassification Performance
dc.subjectFeature Selection
dc.subjectHybrid Structure
dc.titleFeature Selection using FFS and PCA in biomedical data classification with AdaBoost-SVMen_US
dc.typeArticleen_US

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