A Stacking-based Ensemble Learning Method for Outlier Detection

dc.contributor.authorAbro, Abdul Ahad
dc.contributor.authorUgur, Aybars
dc.contributor.authorTaşcı, Erdal
dc.date.accessioned2023-01-12T20:27:49Z
dc.date.available2023-01-12T20:27:49Z
dc.date.issued2020
dc.departmentN/A/Departmenten_US
dc.description.abstractOutlier detection is considered as one of the crucial research areas for data mining. Many methods have been studied widely and utilized for achieving better results in outlier detection from existing literature; however, the effects of these few ways are inadequate. In this paper, a stacking-based ensemble classifier has been proposed along with four base learners (namely, Rotation Forest, Random Forest, Bagging and Boosting) and a Meta-learner (namely, Logistic Regression) to progress the outlier detection performance. The proposed mechanism is evaluated on five datasets from the ODDS library by adopting five performance criteria. The experimental outcomes demonstrate that the proposed method outperforms than the conventional ensemble approaches concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. This method can be used for image recognition and machine learning problems, such as binary classification.en_US
dc.identifier.doi10.17694/bajece.679662
dc.identifier.endpage185en_US
dc.identifier.issn2147-284X
dc.identifier.issue2en_US
dc.identifier.startpage181en_US
dc.identifier.trdizinid468365en_US
dc.identifier.urihttps://doi.org/10.17694/bajece.679662
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/468365
dc.identifier.urihttps://hdl.handle.net/11454/80352
dc.identifier.volume8en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofBalkan Journal of Electrical and Computer Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleA Stacking-based Ensemble Learning Method for Outlier Detectionen_US
dc.typeArticleen_US

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