Selection of Support Vector Machines based classifiers for credit risk domain

This paper describes an approach for credit risk evaluation based on linear Support Vector Machines classifiers, combined with external evaluation and sliding window testing, with focus on application on larger datasets. It presents a technique for optimal linear SVM classifier selection based on particle swarm optimization technique, providing significant amount of focus on imbalanced learning issue. It is compared to other classifiers in terms of accuracy and identification of each class. Experimental classification performance results, obtained using real world financial dataset from SEC EDGAR database, lead to conclusion that proposed technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network, and thus be can be an appealing option for future development of real credit risk evaluation models.