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Title: Credit Card Fraud Detection by Implementing Machine Learning techniques
Authors: Prusti, Debachudamani
Padmanabhuni, S S Harshini
Rath, Santanu Kumar
Keywords: Credit card fraud
Classification Techniques
Fraud detection
Prediction accuracy
Issue Date: Mar-2019
Citation: 1st International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND), Kurukshetra, India, 3 - 4 March 2019
Abstract: Application 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.
Description: Copyright of this document belongs to proceedings publisher.
Appears in Collections:Conference Papers

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