A Hybrid of Particle Swarm Optimization and Ensemble Learning for Credit Risk Assessment

In this paper, we present a new approach which combines particle swarm optimization (PSO) with ensemble techniques to study credit risk assessment problems. In each iteration of the proposed method, PSO is used to solve feature subset selection problems and then nearest neighbor classifiers classify credit risk. Finally, all individual classification outputs are combined to generate the final aggregated outputs using ensemble techniques. The algorithm is applied to classify credit risk using benchmark data sets from UCI databases. The experimental results demonstrate that our proposed method lets to achieve better results than the existing methods in terms of solution quality.