Knowledge discovery using neural approach for SMEs credit risk analysis problem in Turkey
This study proposes a knowledge discovery method that uses multilayer perceptron (MLP) based neural rule extraction (NRE) approach for credit risk analysis (CRA) of real-life small and medium enterprises (SMEs) in Turkey. A feature selection and extraction stage is followed by neural classification that produces accurate rule sets. In the first stage, the feature selection is achieved by decision tree (DT), recursive feature extraction with support vector machines (RFE-SVM) methods and the feature extraction is performed by factor analysis (FA), principal component analysis (PCA) methods. It is observed that the RFE-SVM approach gave the best result in terms of classification accuracy and minimal input dimension. Among various classifiers k-NN, MLP and SVM are compared in classification experiments. Then, the Continuous/Discrete Rule Extractor via Decision Tree Induction (CRED) algorithm is used to extract rules from the hidden units of a MLP for knowledge discovery. Here, the MLP makes a decision for customers as being good or bad and reveals the rules obtained at the final decision. In the experiments, Turkish SME database has 512 samples. The proposed approach validates the claim that is a viable alternative to other methods for knowledge discovery.
multilayer perceptron (mlp) credit risk analysis (cra) small and medium enterprises (smes) continuous/discrete rule extractor via decision tree induction (cred) support vector machines (svm) k-nearest neighbor (k-nn)