This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit scoring model perform more effectively, which comprises three major stages. Firstly, it conducts preprocessing for collected credit data. Then, an efficient feature selection algorithm based on adaptive elastic net is employed to reduce the weakly related or uncorrelated variables to get high-quality training data. Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier. Finally, a new weighting method for single ELM classifiers in the ensemble model is established with respect to their classification accuracy based on generalized fuzzy soft sets (GFSS) theory. A novel cosine-based distance measurement algorithm of GFSS is also proposed to calculate the weights of each ELM classifier. To confirm the efficiency of the proposed ensemble credit scoring model, we implemented experiments with real-world credit data sets for comparison. The process of analysis, outcomes, and mathematical tests proved that the proposed model is capable of improving the effectiveness of classification in average accuracy, area under the curve (AUC), H-measure, and Brier’s score compared to all other single classifiers and ensemble approaches.
A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets
