Abstract
The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process. Both methods try to compensate the class imbalance distribution, however, it is necessary to consider the effect that each method produces in the training process of the Multilayer Perceptron (MLP). The experimental results shows the negative and positive effects that each of these approaches has on the MLP behavior.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Visa, S.: Issues in mining imbalanced data sets - a review paper. In: Artificial Intelligence and Cognitive Science Conference, pp. 67–73 (2005)
Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intelligent Data Analysis 6, 429–449 (2002)
Lawrence, S., Burns, I., Back, A., Tsoi, A.C., Giles, C.L.: Neural network classification and prior class probabilities. In: Orr, G., Müller, K.-R., Caruana, R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 299–314. Springer, Heidelberg (1998)
Kubat, M., Matwin, S.: Detection of oil-spills in radar images of sea surface. Machine Learning (30), 195–215 (1998)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip Kegelmeyer, W.: Smote: Synthetic minority over-sampling technique. J. Artif. Intell. Res. (JAIR) 16, 321–357 (2002)
Japkowicz, N., Myers, C., Gluck, M.: A novelty detection approach to classification. In: Proceedings of the Fourteenth Joint Conference on Artificial Intelligence, pp. 518–523 (1995)
Anand, R., Mehrotra, K., Mohan, C.K., Ranka, S.: Efficient classification for multiclass problems using modular neural networks. IEEE Transactions on Neural Networks 6(1), 117–124 (1995)
Bruzzone, L., Serpico, S.B.: Classification of imbalanced remote-sensing data by neural networks. Pattern Recognition Letters 18, 1323–1328 (1997)
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)
Visa, S., Ralescu, A.: Learning imbalanced and overlapping classes using fuzzy sets. In: Workshop on Learning from Imbalanced Datasets(ICML 2003), pp. 91–104 (2003)
Prati, R.C., Batista, G.E.A.P.A., Monard, M.C.: Class imbalances versus class overlapping: An analysis of a learning system behavior. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 312–321. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alejo, R., Toribio, P., Sotoca, J.M., Valdovinos, R.M., Gasca, E. (2011). Resampling Methods versus Cost Functions for Training an MLP in the Class Imbalance Context. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-21090-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21089-1
Online ISBN: 978-3-642-21090-7
eBook Packages: Computer ScienceComputer Science (R0)