Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3273
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPrusti, Debachudamani-
dc.contributor.authorPadmanabhuni, S S Harshini-
dc.contributor.authorRath, Santanu Kumar-
dc.date.accessioned2019-04-01T10:13:35Z-
dc.date.available2019-04-01T10:13:35Z-
dc.date.issued2019-03-
dc.identifier.citation1st International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND), Kurukshetra, India, 3 - 4 March 2019en_US
dc.identifier.urihttp://hdl.handle.net/2080/3273-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractApplication of machine learning techniques for fraud detection in the credit card operations has been an important component of research in the domain of digital transactions. The evolution of various machine learning techniques like classification and clustering have shown the requirement for application of related algorithms in detecting frauds of credit card transactions. In this study, we have proposed the application of various classification techniques by using machine learning algorithms for detecting the accuracy of the fraud detection. We have implemented some commonly considered classification methods used for a large volume of data. The different algorithms we have evaluated are Na¨ıve Bayes classifier, Extreme learning machine (ELM), K-Nearest Neighbor (K-NN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). We have proposed a model by hybridizing SVM, K-NN and MLP models, in which the prediction accuracy has improved significantly.en_US
dc.subjectCredit card frauden_US
dc.subjectClassification Techniquesen_US
dc.subjectFraud detectionen_US
dc.subjectPrediction accuracyen_US
dc.titleCredit Card Fraud Detection by Implementing Machine Learning techniquesen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2019_MIND_DPrusti_Creditcard.pdfConference paper334.31 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.