Abstract
In this paper we discuss problems of constructing classifiers from imbalanced data. We describe a new approach to selective pre-processing of imbalanced data which combines local over-sampling of the minority class with filtering difficult examples from the majority classes. In experiments focused on rule-based and tree-based classifiers we compare our approach with two other related pre-processing methods – NCR and SMOTE. The results show that NCR is too strongly biased toward the minority class and leads to deteriorated specificity and overall accuracy, while SMOTE and our approach do not demonstrate such behavior. Analysis of the degree to which the original class distribution has been modified also reveals that our approach does not introduce so extensive changes as SMOTE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Batista, G., Prati, R., Monard, M.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 6(1), 20–29 (2004)
Chawla, N.: Data mining for imbalanced datasets: An overview. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 853–867. Springer, Heidelberg (2005)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic Minority Over-sampling Technique. J. of Artifical Intelligence Research 16, 341–378 (2002)
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis 6(5), 429–450 (2002)
Kubat, M., Matwin, S.: Adressing the curse of imbalanced training sets: one-side selection. In: Proc. of the 14th Int. Conf. on Machine Learning, pp. 179–186 (1997)
Laurikkala, J.: Improving identification of difficult small classes by balancing class distribution. Tech. Report A-2001-2, University of Tampere (2001)
Stefanowski, J.: The rough set based rule induction technique for classification problems. In: Proc. of the 6th European Conference on Intelligent Techniques and Soft Computing EUFIT 1998, Aaachen, pp. 109–113 (1998)
Stefanowski, J., Wilk, S.: Rough sets for handling imbalanced data: combining filtering and rule-based classifiers. Fundamenta Informaticae 72, 379–391 (2006)
Stefanowski, J., Wilk, S.: Improving Rule Based Classifiers Induced by MODLEM by Selective Pre-processing of Imbalanced Data. In: Proc. of the RSKD Workshop at ECML/PKDD, Warsaw, pp. 54–65 (2007)
Van Hulse, J., Khoshgoftarr, T., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: Proceedings of ICML 2007, pp. 935–942 (2007)
Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter 6(1), 7–19 (2004)
Wilson, D.R., Martinez, T.: Reduction techniques for instance-based learning algorithms. Machine Learning Journal 38, 257–286 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stefanowski, J., Wilk, S. (2008). Selective Pre-processing of Imbalanced Data for Improving Classification Performance. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-85836-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85835-5
Online ISBN: 978-3-540-85836-2
eBook Packages: Computer ScienceComputer Science (R0)