On the Use of a Combination Approach to Automated Knowledge Acquisition Based on Neural Networks and Fuzzy Logic with Regard to Credit Scoring Problems

Summary

This paper presents an application of automated knowledge acquisition in classification problems. The problem under examination is the classification of a sample of 130 enterprises into 5 different categories of credit risk. The data are both numerical and linguistic in nature, adding up to a total of 70 attributes. All data are obtained from an application form, which the company completes at a bank when seeking a loan and the only field which is provided by the bank is the final class. This classification task deals with statistical models, which try to give a clear view of the importance of every attribute by assigning a weight to it. Then, by multiplying each weight with the value of each corresponding attribute, the method produces a score. According to the range of the score the bank makes its decision. The problem, however, is that this method is very time consuming as it requires a long adaptation period in order to achieve the right results. The alternative solution presented in this paper is a combination of the fuzzy c-means clustering algorithm in conjunction with the Kohonen artificial neural network, which has a self-organizing Kohonen map. The basic idea behind this combination is to take advantage of the benefits offered by the two individual methods mentioned above and to compensate for each method’s short-comings. The c-means process can be integrated into the Kohonen algorithm by replacing the learning rate with membership values thus combining the fuzzy c-means algorithm with the structure and adaptive rules of the Kohonen network.