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Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 8 Sayı: 2

A Stacking-based Ensemble Learning Method for Outlier Detection

Abdul Ahad Abro(EGE ÜNİVERSİTESİ)0000-0002-3591-9231
Erdal Taşcı(EGE ÜNİVERSİTESİ)0000-0001-6754-2187
Aybars Ugur(EGE ÜNİVERSİTESİ)0000-0003-3622-7672
Pages
181–185
Publication Date
2020-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This method can be used for image recognition and machine learning problems, such as binary classification.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Outlier detection is considered as one of the crucial research areas for data mining.
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Key Findings & Data: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.
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Conclusion & Impact:This method can be used for image recognition and machine learning problems, such as binary classification.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Outlier detection · Ensemble learning · Machine Learning

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 (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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Cite This Article

Abro, A. A., Taşcı, E., Ugur, A. (2020). A Stacking-based Ensemble Learning Method for Outlier Detection. Balkan Journal of Electrical and Computer Engineering, Cilt: 8 Sayı: 2, 181–185. https://doi.org/10.17694/bajece.679662

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