C4.5: programs for machine learning
C4.5: programs for machine learning
Computational Economics - Computational Studies at Stanford
Data mining: concepts and techniques
Data mining: concepts and techniques
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
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
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This study presents a neural approach which cascades a neural classifier which is multilayer perceptron (MLP) and a neural rule extractor (NRE) for real-life Small and Medium Enterprises (SMEs) in Turkey. In feature selection stage, decision tree (DT), recursive feature extraction (RFE), factor analysis (FA), principal component analysis (PCA) methods are implemented. In this stage, the RFE approach gave the best result in terms of classification accuracy and minimal input dimension. Then, in classification stage, a MLP that is used for preprocessing is followed by a NRE. The MLP makes a decision for customers as being "good" or "bad" and the NRE reveals the rules how the classifier reached at the final decision. In the experiments, Turkish SME database has 512 samples. The proposed approach compared with k-NN and SVM classifiers. It was observed that the MLP-NRE was slightly better than SVM and local k-NN.