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2020 | OriginalPaper | Buchkapitel

Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning

verfasst von : Rasha Kashef, Michael Gencarelli, Ahmed Ibrahim

Erschienen in: Combating Security Challenges in the Age of Big Data

Verlag: Springer International Publishing

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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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Titel: Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning
verfasst von: Rasha Kashef
Michael Gencarelli
Ahmed Ibrahim
Verlag: Springer International Publishing
Buch: Combating Security Challenges in the Age of Big Data
Print ISBN: 978-3-030-35641-5

Electronic ISBN: 978-3-030-35642-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-35642-2_3

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