Javier De Andrés

TL;DR: This research proposes a hybrid system which combines fuzzy clustering and MARS, and shows that the hybrid model outperforms the other systems, both in terms of the percentage of correct classifications and in Terms of the profit generated by the lending decisions.

TL;DR: A comparative study of the performance of a number of classificatory devices, both parametric (LDA and Logit) and non-parametric (perceptron neural nets and fuzzy-rule-based classifiers), using a Monte Carlo simulation-based approach to measure the average effects of sample size variations on the predictive performance of each classifier.

TL;DR: A hybrid method in which sound companies are divided in clusters using Self Organized Maps and then each cluster is replaced by a director vector which summarizes all of them and the results show that the proposed hybrid approach is much more accurate than the benchmark techniques for the identification of the bankrupt companies.

TL;DR: The results show that the proposed hybrid approach is much more accurate for the identification of the companies that go bankrupt than other approaches such as a multi-layer perceptron neural network and a simple MARS model.

TL;DR: This proposal replaces traditional indicators (accounting ratios) with the output of a so-called multinorm analysis, which may provide significant improvements in predictive accuracy, both in linear and nonlinear classifiers.