Abstract
Outliers, defined as data samples markedly different from the rest of their kind, play an important role in modern pattern recognition and data analysis systems. Outlier treatment usually invokes reasoning about the unknown (irregular) using concepts and features pertaining to the known (regular) samples, naturally requires tools for handling uncertainty or ambiguity, incorporates multi-layered approximate reasoning structures, and often relies on an external background knowledge source. Granular Computing and Rough Set theories provide excellent methods and frameworks for such tasks. In this article, we discuss methods for the detection and evaluation of outliers, as well as how to elicit background domain knowledge from outliers using multi-level approximate reasoning schemes.
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Nguyen, T.T. (2007). Outlier Detection: An Approximate Reasoning Approach. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_52
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DOI: https://doi.org/10.1007/978-3-540-73451-2_52
Publisher Name: Springer, Berlin, Heidelberg
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