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Ensembles and locality: Insight on improving software effort estimation., and . Inf. Softw. Technol., 55 (8): 1512-1528 (2013)GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts., , and . IJCNN, page 1-8. IEEE, (2019)Using unreliable data for creating more reliable online learners., and . IJCNN, page 1-8. IEEE, (2012)How to make best use of cross-company data in software effort estimation?, and . ICSE, page 446-456. ACM, (2014)Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis., , , and . Soft Comput., 20 (10): 3889-3914 (2016)The impact of parameter tuning on software effort estimation using learning machines., , and . PROMISE, page 9:1-9:10. ACM, (2013)Measuring Energy Consumption for Web Service Product Configuration., , and . iiWAS, page 224-228. ACM, (2014)Better software analytics via "DUO": Data mining algorithms using/used-by optimizers., , , , and . Empir. Softw. Eng., 25 (3): 2099-2136 (2020)The impact of data difficulty factors on classification of imbalanced and concept drifting data streams., , , , and . Knowl. Inf. Syst., 63 (6): 1429-1469 (2021)BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain., , , , and . IEEE Trans. Evol. Comput., 26 (6): 1380-1393 (2022)