Abstract
In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority over-sampling technique (SMOTE) is one of the over-sampling methods addressing this problem. Based on SMOTE method, this paper presents two new minority over-sampling methods, borderline-SMOTE1 and borderline-SMOTE2, in which only the minority examples near the borderline are over-sampled. For the minority class, experiments show that our approaches achieve better TP rate and F-value than SMOTE and random over-sampling methods.
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Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: Special Issue on Learning from Imbalanced Data Sets. SIGKDD Explorations 6(1), 1–6 (2004)
Weiss, G.: Mining with rarity: A unifying framework. SIGKDD Explorations 6(1), 7–19 (2004)
Ezawa, K.J., Singh, M., Norton, S.W.: Learning Goal Oriented Bayesian Networks for Telecommunications Management. In: Proceedings of the International Conference on Machine Learning, ICML 1996, Bari, Italy, pp. 139–147. Morgan Kaufmann, San Francisco (1996)
Kubat, m., Holte, R., Matwin, S.: Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning 30, 195–215
van den Bosch, A., Weijters, T., van den Herik, H.J., Daelemans, W.: When small disjuncts abound, try lazy learning: A case study. In: Proceedings of the Seventh Belgian-Dutch Conference on Machine Learning, pp. 109–118 (1997)
Zheng, Z., Wu, X., Srihari, R.: Feature Selection for Text Categorization on Imbalanced Data. SIGKDD Explorations 6(1), 80–89 (2004)
Fawcett, T., Provost, F.: Combining Data Mining and Machine Learning for Effective User Profile. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland OR, pp. 8–13. AAAI Press, Menlo Park (1996)
Lewis, D., Catlett, H.J.: Uncertainty Sampling for Supervized Learning. In: Proceedings of the 11th International Conference on Machine Learning, ICML1994, pp. 148–156 (1994)
Bradley, A.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
Kubat, M., Matwin, S.: Addressing the Course of Imbalanced Training Sets: One-sided Selection. In: ICML 1997, pp. 179–186 (1997)
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., Lazarevic, A., Hall, L.O., Bowyer, K.: SMOTEBoost: Improving prediction of the Minority Class in Boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107–119. Springer, Heidelberg (2003)
Gustavo, E.A., Batista, P.A., Ronaldo, C., Prati, Monard, M.C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explorations 6(1), 20–29 (2004)
Estabrooks, A., Jo, T., Japkowicz, N.: A Multiple Resampling Method for Learning from Imbalanced Data Sets. Computational Intelligence 20(1), 18–36 (2004)
Jo, T., Japkowicz, N.: Class Imbalances versus Small Disjuncts. Sigkdd Explorations 6(1), 40–49 (2004)
Guo, H., Viktor, H.L.: Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach. Sigkdd Explorations 6(1), 30–39 (2004)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Joshi, M., Kumar, V., Agarwal, R.: Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements. In: First IEEE International Conference on Data Mining, San Jose, CA (2001)
Wu, G., Chang, E.Y.: Class-Boundary Alignment for Imbalanced Dataset Learning. In: Workshop on Learning from Imbalanced Datasets II, ICML, Washington, DC (2003)
Huang, K., Yang, H., King, I., Lyu, M.R.: Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2004)
Dietterich, T., Margineantu, D., Provost, F., Turney, P. (eds.): Proceedings of the ICML 2000 Workshop on Cost-sensitive Learning (2000)
Manevitz, L.M., Yousef, M.: One-class SVMs for document classification. Journal of Machine Learning Research 2, 139–154 (2001)
Blake, C., Merz, C.: UCI Repository of Machine Learning Databases. Department of Information and Computer Sciences, University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/~MLRepository.html
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1992)
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Han, H., Wang, WY., Mao, BH. (2005). Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_91
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DOI: https://doi.org/10.1007/11538059_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
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