Abstract
The problem of outliers (Anomalies) detection has been generally presented as a single-minded problem, in which outliers are defined as objects that do not conform to a given definition. In this chapter, we propose a novel taxonomy that groups the methods into two categories: (1) quantitative outlier detection and (2) semantic outlier detection. For quantitative outliers, outliers are defined based on a calculated outlier score. For semantic outliers, there is a conceptual meaning behind the outlier based on the context of the dataset, shifting the focus to finding the anomalous class of data. We also discuss the use of the proposed definition of semantic learning in detecting credit card frauds.
CCS CONCEPTS
Computing methodologies → Anomaly detection
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Kashef, R., Gencarelli, M., Ibrahim, A. (2020). Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning. In: Fadlullah, Z., Khan Pathan, AS. (eds) Combating Security Challenges in the Age of Big Data. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-35642-2_3
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