Abstract
A natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noisy patterns that belonging to one class are labelled as members of another. This allows classifier construction to focus on borderline patterns, likely to be the most informative ones. To appropriately define the above subsets, in this work we will use as base classifiers the so–called parallel perceptrons, a novel approach to committee machine training that allows, among other things, to naturally define margins for hidden unit activations. We shall use these margins to define the above pattern types and to iteratively perform subsample selections in an initial training set that enhance classification accuracy and allow for a balanced classifier performance even when class sizes are greatly different.
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References
Auer, P., Burgsteiner, H., Maass, W.: Reducing Communication for Distributed Learning in Neural Networks. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 123–128. Springer, Heidelberg (2002)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth (1983)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic Minority Oversampling Technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Dorronsoro, J., Ginel, F., Sánchez, C., Santa Cruz, C.: Neural Fraud Detection in Credit Card Operations. IEEE Transactions on Neural Networks 8, 827–834 (1997)
Fawcett, T., Provost, F.: Adaptive Fraud Detection. Journal of Data Mining and Knowledge Discovery 1, 291–316 (1997)
Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121, 256–285 (1995)
Kubat, M., Matwin, S.: Addressing the Curse of Imbalanced Training Sets: One- Sided Selection. In: Proceedings of the 14th International Conference on Machine Learning, ICML 1997, Nashville, TN, U.S.A., pp. 179–186 (1997)
Maloof, M.A.: Learning when data sets are imbalanced and when costs are unequal and unknown. In: ICML-2003 Workshop on Learning from Imbalanced Data Sets II (2003)
Murphy, P., Aha, D.: UCI Repository of Machine Learning Databases, Tech. Report, University of Califonia, Irvine (1994)
Nilsson, N.: The Mathematical Foundations of Learning Machines. Morgan Kaufmann, San Francisco (1990)
Swets, J.A.: Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1998)
Weiss, G.M., Provost, F.: The effect of class distribution on classifier learning, Technical Report ML-TR 43, Department of Computer Science, Rutgers University (2001)
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Cantador, I., Dorronsoro, J.R. (2005). Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_6
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DOI: https://doi.org/10.1007/11492542_6
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