| Resumen |
This paper presents the Naïve Associative Classifier with Epsilon disambiguation (NACε),
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 NACε 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 NACε 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.
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