A Stacking-based Ensemble Learning Method for Outlier Detection

Authors : Abdul Ahad ABRO, Erdal TAŞCI, Aybars UGUR

Pages : 181-185

Doi:10.17694/bajece.679662

View : 21 | Download : 10

Publication Date : 2020-04-30

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(namely, Rotation Forest, Random Forest, Bagging and Boosting); and a Meta-learner insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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.
Keywords : Outlier detection, Ensemble learning, Machine Learning, Classification, Data Mining