The Naïve Associative Classifier with Epsilon Disambiguation

This paper presents the Naïve Associative Classifier with Epsilon disambiguation (NAC), an extension of the Naïve Associative Classifier that, by including a procedure to disambiguate classes in regions where Bayes risk is high, has a positive effect on the performance of the classifiers of the associative approach on several datasets belonging to the financial environment, particularly in terms of credit risk. The experiments conducted to test the NAC were based on 12 datasets composed with financial information and associated with five stages of the credit process: promotion, evaluation, granting, monitoring and recovery. Due to the severe imbalance present in most datasets, the performance of the proposed algorithm was measured using the area under the ROC curve. Likewise, 5× 2 stratified cross validation was made and finally a couple of statistical tests were applied to compare the results. After applying the NAC to the datasets, a successful disambiguation of classes was observed. In the real world this fact could help financial institutions to evaluate the credit applications more effectively and thus, contribute to the mitigation of monetary losses derived from the poor quality of the information.