Abstract
This paper makes use of several performance metrics to extend the understanding of a challenging imbalanced classification task. More specifically, we refer to a problem in which the minority class is more represented in the overlap region than the majority class, that is, the overall minority class becomes the majority one in this region. The experimental results demonstrate that the use of a set of appropriate performance measures allows to figure out such an atypical case.
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Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36, 849–851 (2003)
Batista, G.E., Pratti, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6, 20–29 (2004)
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proc. of 23rd Intl. Conf. on Machine Learning, pp. 161–168 (2006)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Chawla, N.V., Japokowicz, N., Kolcz, A.: Special Issue on Learning from Imbalanced Data Sets (Editorial). SIKGDD Explorations 6, 1–6 (2004)
Daskalaki, S., Kopanas, I., Avouris, N.: Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intelligence 20, 381–417 (2006)
Fawcett, T.: ROC graphs with instance-varying costs. Pattern Recognition Letters 27, 882–891 (2006)
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. on Knowledge and Data Engineering 17, 299–310 (2005)
Kubat, M., Matwin, S.: Adressing the curse of imbalanced training sets: one-sided selection. In: Proc. of 14th Intl. Conf. on Machine Learning, pp. 179–186 (1997)
Landgrebe, T.C.W., Paclick, P., Duin, R.P.W.: Precision-recall operating characteristic (P-ROC) curves in imprecise environments. In: Proc. of 18th Intl. Conf. on Pattern Recognition, pp. 123–127 (2006)
Provost, F., Fawcett, T.: Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In: Proc. of 3rd Intl. Conf. on Knowledge Discovery and Data Mining, pp. 43–48 (1997)
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© 2007 Springer Berlin Heidelberg
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García, V., Mollineda, R.A., Sánchez, J.S., Alejo, R., Sotoca, J.M. (2007). When Overlapping Unexpectedly Alters the Class Imbalance Effects. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_63
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DOI: https://doi.org/10.1007/978-3-540-72849-8_63
Publisher Name: Springer, Berlin, Heidelberg
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