Auto insurance fraud detection using unsupervised spectral ranking for anomaly

Auto insurance fraud detection using unsupervised spectral ranking for anomaly

For many data mining problems, obtaining labels is costly and time consuming, if not practically infeasible. In addition, unlabeled data often includes categorical or ordinal features which, compared with numerical features, can present additional challenges. We propose a new unsupervised spectral r...

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Bibliographic Details
Main Authors: Ke Nian, Haofan Zhang, Aditya Tayal, Thomas Coleman, Yuying Li
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2016-03-01
Series:Journal of Finance and Data Science
Subjects:
Unsupervised learning
Fraud detection
Rare class ranking
Similarity measure
Kernels
Spectral clustering
One-class svm
Online Access:http://www.sciencedirect.com/science/article/pii/S2405918816300058

Internet

http://www.sciencedirect.com/science/article/pii/S2405918816300058

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