Ensemble Classification Method for Credit Card Fraud Detection
Inderpreet Kaur1, Mala Kalra2
Inderpreet Kaur,Computer Science & Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India. Mala Kalra, Computer Science & Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India
Manuscript received on 11 August 2019. | Revised Manuscript received on 21 August 2019. | Manuscript published on 30 September 2019. | PP: 423-427 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4213098319/19©BEIESP | DOI: 10.35940/ijrte.C4213.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Credit card frauds are on the rise and are getting smarter with the passage of time. Usually, fraudulent transactions are conducted by stealing the credit card. When the loss of the card is not noticed by the cardholder, a huge loss can be faced by the credit card company. In the existing work, it has been found that the researchers have utilized Voting based method to identify credit card frauds. The problem with voting based method is that they are more complex and more time consuming. In this research work, a hybrid approach based on KNN and Naive Bayes for the detection of credit card frauds. KNN will be used as the base classifier and it will return predicted result. The predicted result will be provided as input to the Naive Bayes classifier which will generate the final result. The proposed model will be compared with existing techniques and the results are analyzed in terms of recall, precision, accuracy and execution time.
Keywords: Credit Card Fraud Detection, Ensemble, Voting.
Scope of the Article: