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Credit risk evaluation is an important decision process to financial institutions. Feature (variable) selection is a key step to many credit evaluation problems and it is often used as a dimension reduction technique to process credit data. However, the traditional correlation-based feature selection (CFS) is a linear analysis method when calculating the correlation coefficient and it cannot deal efficiently with nonlinearly correlated variables. This paper presents an improved approach of nonlinear correlation-based feature selection-Gebelein's maximal correlation-based feature selection (GCFS), based on analysis of CFS and Gebelein's maximal correlation (GMC), to realize the data reduction. Furthermore, an integrated model, GCFS-ANFIS model, is presented combined GCFS with Adaptive Neuro Fuzzy Inference System (ANFIS). The proposed model has been applied to credit evaluation based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of the GCFS-ANFIS model is much better than the ones of the other classic methods.

To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm-Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network's drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.