文章基本信息

标题：A NOVEL PROBABILISTIC BASED FEATURE SELECTION MODEL FOR CREDIT CARD ANOMALY DETECTION
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作者：Y.A.SIVA PRASAD ; DR.G.RAMAKRISHNA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：94
期号：2
出版社：Journal of Theoretical and Applied
摘要：Due to the increase in online financial applications, the fraudulent operations through online transactions have increased rapidly. Also, the anomaly detection in credit card transactions has become equally important in many fields in which the data have high dimensional attributes. Finding noisy anomaly attributes using the conventional models are inefficient and infeasible, as the size and number of instances are large. In this paper, an optimized probabilistic based feature selection model was implemented on credit card fraud detection. An efficient ranked attributes are extracted using the hybrid feature selection algorithm. Experimental results show that proposed system efficiently detects the relevant attributes compared to traditional models in terms of time and dimensions are concerned
关键词：Feature selection algorithm; Fraud detection; Markov model; density distribution.