Abstract
Outlier detection has many practical applications, especially in domains that have scope for abnormal behavior. Despite the importance of detecting outliers, defining outliers in fact is a nontrivial task which is normally application-dependent. On the other hand, detection techniques are constructed around the chosen definitions. As a consequence, available detection techniques vary significantly in terms of accuracy, performance and issues of the detection problem which they address. In this paper, we propose a unified framework for combining different outlier detection algorithms. Unlike existing work, our approach combines non-compatible techniques of different types to improve the outlier detection accuracy compared to other ensemble and individual approaches. Through extensive empirical studies, our framework is shown to be very effective in detecting outliers in the real-world context.
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Nguyen, H.V., Ang, H.H., Gopalkrishnan, V. (2010). Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12026-8_29
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DOI: https://doi.org/10.1007/978-3-642-12026-8_29
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