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.
Highlights
A Stacking-based Ensemble Learning Method for Outlier DetectionAbstract—Outlier detection is considered as one of the crucial research areas for data mining
OUTLIER IS defined as an observation that deviates from other observations or suspicious events that are generated by different mechanisms
We have proposed a framework of a Stackingbased ensemble learning method, including rotation forest, random forest, bagging, boosting and logistic regression
Summary
Abstract—Outlier detection is considered as one of the crucial research areas for data mining. 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 Fmeasure values. This method can be used for image recognition and machine learning problems, such as binary classification
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