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One-class support vector classifiers: A survey
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DOI：10.1016/j.knosys.2020.105754.png)
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Abstract
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Over the past two decades, one-class classification (OCC) becomes very popular due to its diversified applicability in data mining and pattern recognition problems. Concerning to OCC, one-class support vector classifiers (OCSVCs) have been extensively studied and improved for the technology-driven applications; still, there is no comprehensive literature available to guide researchers for future exploration. This survey paper presents an up to date, structured and well-organized review on one-class support vector classifiers. This survey comprises available algorithms, parameter estimation techniques, feature selection strategies, sample reduction methodologies, workability in distributed environment and application domains related to OCSVCs. In this way, this paper offers a detailed overview to researchers looking for the state-of-the-art in this area. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
One-class classification (OCC)
One-class support vector classifiers (OCSVCs)
Parameter estimation
Feature selection
Sample reduction
Distributed environment
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