Credit Card Clients Classification Using Hybrid Guided wheel with Particle Swarm Optimized for Voting Ensemble

Khadija Shazly ^*,1, Nima Khodadadi ²

¹ Faculty of Computer and Information, Mansoura university, Egypt

²University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA.

Emails: khadijashazly@students.mans.edu.eg; nxk682@miami.edu

Abstract

Credit card use is rapidly increasing as a result of the widespread availability of these cards, the ease of making electronic transfers, and the ubiquity of online shopping. But credit card debt poses a serious risk to businesses and governments alike, not to mention individual savers and investors. Consequently, the need for efficient, timely, and reliable ways to anticipate credit card risk has grown. In this study, we offer a framework that combines three classifiers, namely, support vector machines, multilayer perceptron and decision trees, to improve the network's accuracy. The proposed strategy is shown to be very competitive with others through simulation.

Keywords: Credit scoring; Credit card; Machine learning; Classification; Metaheuristic optimization; K-Nearest neighbor; Random Forest; Support vector machines