Metaheuristic-Based Feature Optimization for Portfolio Management

Abstract

In the last two to three decades, use of credit cards is increasing rapidly due to fast economic growth in developing countries and worldwide globalization issues. Financial institutions like banks are facing a very tough time due to fast-rising cases of credit card loan payment defaulters. The banking institution is constantly searching for the perfect mechanisms or methods to identify possible defaulters among the whole set of credit card users. In this chapter, the most important features of a credit card holder are identified from a considerably large set of features using metaheuristic algorithms. In this work, a standard data set archived in UCI repository of credit card payments of Taiwan is used. Metaheuristic algorithms like particle swarm optimization, ant colony optimization, and simulated annealing are used to identify the significant sets of features from the given data set. Support vector machine classifier is used to identify the class in this two-class (loan defaulter or not) problem.