Genetic algorithm-based heuristic for feature selection in credit risk assessment

In this paper, an advanced novel heuristic algorithm is presented, the hybrid genetic algorithm with neural networks (HGA-NN), which is used to identify an optimum feature subset and to increase the classification accuracy and scalability in credit risk assessment. This algorithm is based on the following basic hypothesis: the high-dimensional input feature space can be preliminarily restricted to only the important features. In this preliminary restriction, fast algorithms for feature ranking and earlier experience are used. Additionally, enhancements are made in the creation of the initial population, as well as by introducing an incremental stage in the genetic algorithm. The performances of the proposed HGA-NN classifier are evaluated using a real-world credit dataset that is collected at a Croatian bank, and the findings are further validated on another real-world credit dataset that is selected in a UCI database. The classification accuracy is compared with that presented in the literature. Experimental results that were achieved using the proposed novel HGA-NN classifier are promising for feature selection and classification in retail credit risk assessment and indicate that the HGA-NN classifier is a promising addition to existing data mining techniques.