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Noise correction using bayesian multiple imputation.

, , , and . IRI, page 478-483. IEEE Systems, Man, and Cybernetics Society, (2006)

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Building Useful Models from Imbalanced Data with Sampling and Boosting., , , and . FLAIRS, page 306-311. AAAI Press, (2008)A Hybrid Approach to Cleansing Software Measurement Data., , and . ICTAI, page 713-722. IEEE Computer Society, (2006)A Comparison of Software Fault Imputation Procedures., , and . ICMLA, page 135-142. IEEE Computer Society, (2006)An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data., , , and . IRI, page 651-658. IEEE Systems, Man, and Cybernetics Society, (2007)Noise correction using bayesian multiple imputation., , , and . IRI, page 478-483. IEEE Systems, Man, and Cybernetics Society, (2006)Learning from Software Quality Data with Class Imbalance and Noise., , , and . SEKE, page 487-. Knowledge Systems Institute Graduate School, (2007)RUSBoost: Improving classification performance when training data is skewed., , , and . ICPR, page 1-4. IEEE Computer Society, (2008)An empirical study of the classification performance of learners on imbalanced and noisy software quality data., , , and . Inf. Sci., (2014)A Comparative Study of Data Sampling and Cost Sensitive Learning., , , and . ICDM Workshops, page 46-52. IEEE Computer Society, (2008)Improving Learner Performance with Data Sampling and Boosting., , , and . ICTAI (1), page 452-459. IEEE Computer Society, (2008)