Robust Classification via Support Vector Machines

Alexandru V. Asimit, Ioannis Kyriakou, Simone Santoni, Salvatore Scognamiglio and Rui Zhu
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Alexandru V. Asimit: Faculty of Actuarial Science & Insurance, Bayes Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK
Ioannis Kyriakou: Faculty of Actuarial Science & Insurance, Bayes Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK
Simone Santoni: Faculty of Management, Bayes Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK
Salvatore Scognamiglio: Department of Management and Quantitative Sciences, University of Naples Parthenope, Via Generale Parisi 13, 80132 Naples, Italy
Rui Zhu: Faculty of Actuarial Science & Insurance, Bayes Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK

Risks, 2022, vol. 10, issue 8, 1-25

Abstract: Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust support vector machine classifiers under feature data uncertainty via two probabilistic arguments. The first classifier, Single Perturbation , reduces the local effect of data uncertainty with respect to one given feature and acts as a local test that could confirm or refute the presence of significant data uncertainty for that particular feature. The second classifier, Extreme Empirical Loss , aims to reduce the aggregate effect of data uncertainty with respect to all features, which is possible via a trade-off between the number of prediction model violations and the size of these violations. Both methodologies are computationally efficient and our extensive numerical investigation highlights the advantages and possible limitations of the two robust classifiers on synthetic and real-life insurance claims and mortgage lending data, but also the fairness of an automatized decision based on our classifier.

Keywords: binary robust classification; insurance fraud prediction; mortgage lending prediction; support vector machine (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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