Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring

In this paper, we evaluate and contrast two fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimisation and boosting whereas the second classifier is based on a fuzzy neural network. We show that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5 algorithm that we included as a reference classifier. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible than the descriptive fuzzy rules inferred by the neurofuzzy classifier.