A Stacking-based Ensemble Learning Method for Outlier Detection
Küçük Resim Yok
Tarih
2020
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Kaynak
Balkan Journal of Electrical and Computer Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
8
Sayı
2
