Optimization Of Neural Classifiers Based On Bayesian Decision Boundaries And Idle Neurons Pruning

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.163387390Castellano, G., Fanelli, A.M., Pelillo, M., An iterative pruning algorithms for feedforward neural networks (1997) IEEE Trans. Neural Networks, 8 (3), pp. 519-531Cavalcanti, H.M.C., (2000) Feature Extraction Based on Decision Boundary Using Neural Networks, , Master's thesis, UNICAMPCavalcanti, H.M.C., Ling, L.L., Fast and efficient feature extraction based on bayesian decision boundaries (2000) Proc. Intl. Conf. Pattern RecognitionConover, W.J., (1980) Practical Nonparametric Statistics, , John Wiley and Sons, 2 editionKamin, E.D., A simple procedure for pruning back-propagation trained neural networks (1990) IEEE Trans. Neural Networks, 1 (2), pp. 239-242LeCun, Y.L., Denker, J.S., Solla, S.A., Optimal brain damage (1990) Advances in Neural Information Processing, pp. 598-605. , D. S. TouretzkyLippmann, R.P., Pattern classification using neural networks (1989) IEEE Comm. Magazine, pp. 47-64Mao, J., Mohiuddin, K., Jain, A.K., Parsimonious network design and feature selection through node pruning (1994) Proc. 12th Intl. Conf. Pattern Recognition, 2, pp. 622-624. , Jerusalem, ILMurase, K., Matsunaga, Y., Nakade, Y., A backpropagation algorithm which automatically determines the number of association units Proc. Intl. Conf. Neural Networks, pp. 783-788. , Singapore, apud [1]Murphy, P.M., Aha, D.M., (1994) UCI Repository of Machine Learning Databases, , http://www.ics.uci.edu/mlearn/MLRepository.html, Uniwrsity of California, Department of Information and Computer Science, Irvine, CAPark, Y.R., Murray, T.J., Chen, C., Predicting sun spots using a layered perceptron neural network (1996) IEEE Trans. Neural Networks, 7 (2), pp. 501-505Reed, R., Pruning algorithms - A survey (1993) IEEE Trans. Neural Networks, 4 (5), pp. 740-747Silvestre, M.R., Ling, L.L., Reducting the architecture of a network by gap of saliency of the neurons (1998) Anais v Simpósio Brasileiro de Redes Neurain, pp. 91-96. , Belo Horizonte, BrTukey, J.W., (1977) Exploratory Data Analysis, , Addison-Wesley, Massachusett