Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.679662 Artificial Intelligence Yapay Zeka A Stacking-based Ensemble Learning Method for Outlier Detection https://orcid.org/0000-0002-3591-9231 Abro Abdul Ahad EGE ÜNİVERSİTESİ https://orcid.org/0000-0001-6754-2187 Taşcı Erdal EGE ÜNİVERSİTESİ https://orcid.org/0000-0003-3622-7672 Ugur Aybars EGE ÜNİVERSİTESİ 04 30 2020 8 2 181 185 01 24 2020 04 14 2020 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Outlier 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.

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