Variable selection in the prediction of business failure using genetic programming

Abstract

This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes into account the relative frequency of occurrence of the explanatory variables in the evolved solutions, as well as the statistical relevance of that frequency. For a better evaluation of the proposed method and its comparison with other well-tested and widely used variable selection methods, the prediction of business failure in three temporal horizons (1, 5 and 9 years prior to failure) is considered. Additionally, a comparison of the sets of variables selected with different feature selection methods is performed, also considering different classifiers in the comparison, among which Genetic Programming is included as a classifier. The results indicate that the proposed method (using Genetic Programming as a variable selection method) is superior to the most tested and widely used methods analyzed, and this superiority increases if Genetic Programming is also used as a classification method.