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Handling imbalanced datasets: A review
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Automatically countering imbalance and its empirical relationship to cost
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Chawla, N.V., Cieslak, D.A., Hall, L.O., Joshi, A.: Automatically countering imbalance and its empirical relationship to cost. Utility-Based Data Mining: A Special issue of the International Journal Data Mining and Knowledge Discovery (2008)
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Efficient Optimization of Performance Measures by Classifier Adaptation
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An Extensive Empirical Study of Feature Selection Metrics for Text Classification
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SMOTE: Synthetic minority over-sampling technique
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Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321-357 (2002)
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SMOTEBoost: Improving Prediction of the Minority Class in Boosting
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Knowledge Discovery in Databases: PKDD 2003
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Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: Improving Prediction of the Minority Class in Boosting. In: Lavraè, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107-119. Springer, Heidelberg (2003) (Pubitemid 37231089)
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