A modified hybrid feature-selection method based on a filter and wrapper approach for credit risk forecasting

Sifting through features to find the most related ones is known as feature selection. This paper introduces a feature-selection technique based on a modified approach in order to improve classification performance using fewer features: the chi-squared with recursive feature elimination (χ2-RFE) method. It combines χ2 as a filter approach with recursive feature elimination as a wrapper approach. The algorithm developed for the χ2-RFE method is superior to six other algorithms in measures of average performance, with acceptable computing time. This is demonstrated by application to a data set of Chinese listed companies with a sample size of 47 172 and 535 characteristics, and the efficacy of the χ2-RFE algorithm is further confirmed by an experiment on a German data set with a sample size of 1000 and 24 characteristics. Since it can be challenging to achieve high accuracy and good performance in measures related to imbalanced data with only a few features, we extensively analyze the potential of our modified feature-selection framework, χ2-RFE, to provide a solution.