Application of particle swarm optimization to credit risk assessment

In this paper, we apply particle swarm optimization (PSO) to solve feature subset selection problems. The proposed PSO algorithm is combined with nearest neighbor classifiers. The algorithm is applied to classify credit risk using benchmark data sets from UCI databases. The experimental results presented in the paper demonstrate that the application of our proposed method lets to achieve better results than the existing methods in terms of solution quality and computational efficiency.