Computational Economics - Computational Studies at Stanford
Immunological Bioinformatics (Computational Molecular Biology)
Immunological Bioinformatics (Computational Molecular Biology)
International Journal of Intelligent Systems in Accounting and Finance Management
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Enhanced Recursive Feature Elimination
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Credit Risk Assessment Using Rough Set Theory and GA-Based SVM
GPC-WORKSHOPS '08 Proceedings of the 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
Credit Risk Evaluation Using Support Vector Machine with Mixture of Kernel
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
An Analysis of Support Vector Machines for Credit Risk Modeling
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
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.