Abstract
In this paper, the behavior of Modular and Non-Modular Neural Networks trained with the classical backpropagation algorithm in batch mode and applied to classification problems with Multi-Class imbalance is studied. Three different cost functions are introduced in the training algorithm in order to solve the problem in four different databases. The proposed strategies show an improvement in the classification accuracy with three different types of Neural Networks.
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References
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: a Systematic Study. Intelligent Data Analysis 6, 429–449 (2002)
Kotsiantis, S., Pintelas, P.: Mixture of Expert Agents for Handling Imbalanced Data Sets. Annals of Mathematics and Computing & TeleInformatics 1, 46–55 (2003)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic Minority Over-Sampling Technique. J. Artif. Intell. Res. (JAIR) 16, 321–357 (2002)
Anand, R., Mehrotra, K., Mohan, C.K., Ranka, S.: Efficient Classification for Multiclass Problems Using Modular Neural Networks. IEEE Transactions on Neural Networks 6, 117–124 (1995)
Zhou, Z.H., Liu, X.Y.: Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. IEEE Transactions on Knowledge and Data Engineering 18, 63–77 (2006)
Bruzzone, L., Serpico, S.B.: Classification of Imbalanced Remote-Sensing Data by Neural Networks. Pattern Recognition Letters 18, 1323–1328 (1997)
Auda, G., Kamel, M.: Modular Neural Network Classifiers: A Comparative Study. Journal of Intelligent and Robotic Systems 21, 117–129 (1998)
Eric, R., Gawthrop, P.: Modular Neural Networks: A State of the Art. Technical Report CSC-95026, Centre for System and Control. Faculty of mechanical Engineering, University of Glasgow, UK (1995)
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, S.J.: Adaptive Mixtures of Local Experts. Neural Computation 3, 79–87 (1991)
Looney, C.: Pattern Recognition Using Neuronal Networks - Theory and Algorithms for Engineers and Scientists, 1st edn. Oxford University Press, New York (1997)
Ding, C., Xiang, S.Q.: From Multilayer Perceptrons to Radial Basis Function Networks: A Comparative Study. In: EEE. Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 69–74 (2004)
Pao, Y.H., Park, G.H., Sobajic, D.J.: Learning and Generalization Characteristics of the Random Vector Functional-Link Net. Neurocomputing 6, 163–180 (1994)
Anand, R., Mehrotra, K.G., Mohan, C.K., Ranka, S.: An Improved Algorithm for Neural Network Classification of Imbalanced Training Sets. IEEE Transactions on Neural Networks 4, 962–969 (1993)
Alejo, R., Garcia, V., Sotoca, J.M., Mollineda, R.A., Sanchez, J.S.: Improving the Performance of the RBF Neural Networks with Imbalanced Samples. In: 9th International Work-Conference on Artificial Neural Networks, pp. 162–169. Springer, Heidelberg (2007)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
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Alejo, R., Sotoca, J.M., Valdovinos, R.M., Casañ, G.A. (2009). The Multi-Class Imbalance Problem: Cost Functions with Modular and Non-Modular Neural Networks. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_44
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DOI: https://doi.org/10.1007/978-3-642-01216-7_44
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