A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity
In this paper, we introduce a new approach for supervised classification to handle mixed-data (i.e., categorical, binary, and continuous) data structures using a hybrid radial basis function neural networks (HRBF-NN). HRBF-NN supervised classification combines regression trees, ridge regression, and the genetic algorithm (GA) with radial basis function (RBF) neural networks (NN) along with information complexity (ICOMP) criterion as the fitness function to carry out both classification and subset selection of best predictors which discriminate between the classes. In this manner, we reduce the dimensionality of the data and at the same time improve classification accuracy of the fitted predictive model. We apply HRBF-NN supervised classification to a real benchmark credit approval mixed-data set to classify the customers into good/bad classes for credit approval. Our results show the excellent performance of HRBF-NN method in supervised classification tasks.
KeywordsRadial Basis Function Classification Tree Radial Basis Function Neural Network Supervise Classification Saturated Model
This paper was invited as a keynote presentation by Prof. Bozdogan at the European Conference on Data Analysis (ECDA-2013) at the University of Luxembourg in Luxembourg during July 10–12, 2013. Prof. Bozdogan extents his gratitude to the conference organizers: Professors Sabine Krolak-Schwerdt, Matthias Bömer, and Berthold Lausen.
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. H. Petrox & F. Csaki, (Eds.), Second International Symposium on Information Theory (pp. 267–281). Budapest: Academiai Kiado.Google Scholar
- Akbilgic, O. (2011). Variable selection and prediction using hybrid radial basis function neural networks: A case study on stock markets. PhD thesis, Istanbul University.Google Scholar
- Akbilgic, O., Bozdogan, H., & Balaban, M. E. (2013). A novel hybrid RBF neural network model as a forecaster. Statistics and Computing. doi:10.1007/s11222-013-9375-7.Google Scholar
- Anderson, R. (2007). The credit scoring toolkit. Oxford: Oxford University Press.Google Scholar
- Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.Google Scholar
- Bozdogan, H. (1994). Mixture-model cluster analysis using a new informational complexity and model selection criteria. In H. Bozdogan (Ed.), Multivariate Statistical Modeling, Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach (Vol. 2, pp. 69–113). North-Holland: SpringerGoogle Scholar
- Bozdogan, H. (2004) Intelligent statistical data mining with information complexity and genetic algorithms. In H. Bozdogan (Ed.) Statistical data mining and knowledge discovery (pp. 15–56). Boca Raton: Chapman and Hall/CRCGoogle Scholar
- Credit Approval Data Set by UCI MAchine Learning Repository. http://archive.ics.uci.edu/ml/datasets/Credit+Approval. Cited April 26, 2013
- Eiben, A. E., & Smith, J. E. (2010). Introduction to evolutionary computing. New York: Springer.Google Scholar
- Flach, P. A., Hernandez-Orallo, J., & Ferri, C. (2013). Comparing apples and oranges: Towards commensurate evaluation metrics in classification. Keynote lecture presented in the European Conference on Data Analysis (ECDA-2013), Luxembourg.Google Scholar
- Liu, Z., & Bozdogan, H. (2004) Improving the performance of radial basis function classification using information criteria. In H. Bozdogan (Ed.), Statistical data mining and knowledge discovery (pp. 193–216). Boca Raton: Chapman and Hall/CRC.Google Scholar
- Sutton, C. D. (2005). Classification and regression trees, bagging, and boosting. In Handbook of statistics Vol. 24, pp. 303–329. Elsevier B.V. doi: 10.1016/s0169-716(04)24004-4.Google Scholar