Learning Decision Trees for Unbalanced Data

Author:

Cieslak David A.,Chawla Nitesh V.

Publisher

Springer Berlin Heidelberg

Reference25 articles.

1. Japkowicz, N.: Class Imbalance Problem: Significance & Strategies. In: International Conference on Artificial Intelligence (ICAI), pp. 111–117 (2000)

2. Kubat, M., Matwin, S.: Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. In: International Conference on Machine Learning (ICML), pp. 179–186 (1997)

3. Batista, G., Prati, R., Monard, M.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explorations 6(1), 20–29 (2004)

4. Van Hulse, J., Khoshgoftaar, T., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: ICML, pp. 935–942 (2007)

5. 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)

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