Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.

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Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the

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Abstract-In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. It uses five individual rank based feature selection methods. It proposes a novel rank aggregation algorithm for combining the ranks of the individual feature selection methods of the ensemble. This algorithm uses the rank order along with the rank score of the features in the ranked list of each feature selection method for rank aggregation. The ensemble of multiple feature selection techniques uses the novel rank aggregation algorithm and selects the relevant features using the 80%, 60%, 40% and 20% thresholds from the top of the aggregated ranked list for building the C4.5, MLP, C4.5 based Bagging and MLP based Bagging models. It was observed that the performance of models using the ensemble of multiple feature selection techniques is better than the performance of 5 individual rank based feature selection methods. The average performance of all the models was observed as best for the ensemble of feature selection techniques at 60% threshold. Also, the bagging based models outperformed the individual models most significantly for the 60% threshold. This increase in performance is more significant from the fact that the number of features were reduced by 40% for building the highest performing models. This reduces the data dimensions and hence the overall data size phenomenally for model building. The use of the ensemble of feature selection techniques using the novel aggregation algorithm provided more accurate models which are simpler, faster and easy to interpret.