Meta Classification Technique for Improving Credit Card Fraud Detection

Data mining is the process of automatic classification of cases based on data patterns obtained from a dataset. A number of algorithms have been developed and implemented to extract information and discover knowledge patterns that may be useful for decision support. Credit card frauds occur by online and offline. Due to increase in recent developments in technology fraud transactions also increased. In this work a ensemble method based on the D-TREE, SVM, KNN and FA is proposed for solving transaction data classification problems. Initial solutions are generated at random using D-TREE, SVM, KNN and the FA that tries to optimize the weights of the D-TREE, SVM, and KNN carries out the improvement. Experiments results using CREDITCARD transaction data sets show that the proposed FA-D-TREE, SVM, KNN outperforms the D-TREE, SVM, KNN on datasets. Further comparison with other approaches in the literature shows that the ensemble method is able to minimize the error rate. All results show ensemble FA-D-TREE, SVM, KNN outperforms normal methods.