Abstract

Feature selection algorithms that are based on different single evaluation criterions for determining the subset of features shows varying result sets which lead to inconsistency in ranks. In contrary, Multiple Criteria Decision Making (MCDM) with Fuzzified Feature Selection methodology brings consistency in feature selection ranking with optimal features and improving the classification performance of credit risks. By adopting multiple evaluation criteria inconsistent ranks to Fuzzy Analytic Hierarchy Process (FAHP) for feature selection along with hybrid algorithm (K-Means clustering-Logistic Regression classification) results in enabling Consistent Ranking Feature Selection (CRFS) and significant improvement over classification performance measures. When the proposed methodology is used with two different credit risk data set from the UCI repository, the experimental results show that the optimal features with hybrid algorithm, indicating improvements in the performance of classification in credit risk prediction over the current existing techniques.

Keywords:

Credit risk, feature selection, fuzzy analytic hierarchy process, k-means clustering, logistic regression classification, multiple criteria decision making,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.