Adaptive boosting in ensembles for outlier detection: Base learner selection and fusion via local domain competence

Abstract

Unusual data patterns or outliers can be generated because of human errors, incor-rect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to cre-ate a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are pre-sented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.KEYWORDSadaptive boosting, base learners, heterogeneous ensembles, outlier detection