Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/7878
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dc.contributor.authorGrewal, Jagmann Singh-
dc.contributor.authorNitin [Guided by]-
dc.date.accessioned2022-10-17T06:54:28Z-
dc.date.available2022-10-17T06:54:28Z-
dc.date.issued2014-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/7878-
dc.description.abstractIn recent years, the prevailing data mining concerns people with credit card fraud detection model based on data mining. Since our problem is approached as a classification problem, classical data mining algorithms are not directly applicable. So an alternative approach is made by using general purpose meta heuristic approaches like genetic algorithms. This project is to propose a credit card fraud detection system using genetic algorithm. Genetic algorithms are evolutionary algorithms which aim at obtaining better solutions as time progresses. When a card is copied or stolen or lost and captured by fraudsters it is usually used until its available limit is depleted. Thus, rather than the number of correctly classified transactions, a solution which minimizes the total available limit on cards subject to fraud is more prominent. It aims in minimizing the false alerts using genetic algorithm where a set of interval valued parameters are optimized.en_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectCredit carden_US
dc.subjectDetectionen_US
dc.titleCredit Card Fraud Detectionen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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