ONE-CLASS SUPPORT VECTOR MACHINES APPROACH TO ANOMALY DETECTION

This article presents two-class and one-class support vector machines (SVM) for detection of fraudulent credit card transactions. One-class SVM classification with different kernels is considered for a dataset of fraudulent credit card transactions treating the fraud transactions as outliers. The ef...

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Published inApplied artificial intelligence Vol. 27; no. 5; pp. 351 - 366
Main Authors Hejazi, Maryamsadat, Singh, Yashwant Prasad
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis Group 28.05.2013
Taylor & Francis Ltd
Subjects
Cards
Classification
Classifiers
Comparative analysis
Credit card fraud
Fraud
Kernels
Mathematical analysis
Support vector machines
Vectors (mathematics)
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Summary:This article presents two-class and one-class support vector machines (SVM) for detection of fraudulent credit card transactions. One-class SVM classification with different kernels is considered for a dataset of fraudulent credit card transactions treating the fraud transactions as outliers. The effectiveness of the two-class C-Support Vector Classification (C-SVC) and ν-Support Vector Machines with different kernels are also presented on a fraudulent credit card transactions dataset. We describe and compare the performance of binary classifiers using balanced and imbalanced datasets with one-class SVM classifiers. The results of these methods are demonstrated on a credit card fraud dataset to show the superiority of one-class SVM for the anomaly detection problem.
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ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2013.785791