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Comparison of approaches to alleviate problems with high-dimensional and class-imbalanced data.

, , , and . IRI, page 234-239. IEEE Systems, Man, and Cybernetics Society, (2011)

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Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data., , and . IEEE Trans. Syst. Man Cybern. Part A, 41 (3): 552-568 (2011)RUSBoost: A Hybrid Approach to Alleviating Class Imbalance., , , and . IEEE Trans. Syst. Man Cybern. Part A, 40 (1): 185-197 (2010)Empirical Case Studies in Attribute Noise Detection., and . IEEE Trans. Syst. Man Cybern. Part C, 39 (4): 379-388 (2009)Identifying learners robust to low quality data., , , and . IRI, page 190-195. IEEE Systems, Man, and Cybernetics Society, (2008)An exploration of learning when data is noisy and imbalanced., , and . Intell. Data Anal., 15 (2): 215-236 (2011)Metric Selection for Software Defect Prediction., , , and . International Journal of Software Engineering and Knowledge Engineering, 21 (2): 237-257 (2011)Software quality imputation in the presence of noisy data., , , and . IRI, page 484-489. IEEE Systems, Man, and Cybernetics Society, (2006)A Comparative Study of Threshold-Based Feature Selection Techniques., , and . GrC, page 499-504. IEEE Computer Society, (2010)Building Useful Models from Imbalanced Data with Sampling and Boosting., , , and . FLAIRS, page 306-311. AAAI Press, (2008)Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques., , , and . ICMLA, page 147-152. IEEE Computer Society, (2010)