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autoBagging: Learning to Rank Bagging Workflows with Metalearning.

, , , and . AutoML@PKDD/ECML, volume 1998 of CEUR Workshop Proceedings, page 21-27. CEUR-WS.org, (2017)

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Arbitrated Ensemble for Time Series Forecasting., , , and . ECML/PKDD (2), volume 10535 of Lecture Notes in Computer Science, page 478-494. Springer, (2017)POPSTAR at RepLab 2013: Name Ambiguity Resolution on Twitter., , , , , , , , and . CLEF (Working Notes), volume 1179 of CEUR Workshop Proceedings, CEUR-WS.org, (2013)CHADE: Metalearning with Classifier Chains for Dynamic Combination of Classifiers., , and . ECML/PKDD (1), volume 9851 of Lecture Notes in Computer Science, page 410-425. Springer, (2016)Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles., , , , and . ECML/PKDD (1), volume 11051 of Lecture Notes in Computer Science, page 620-636. Springer, (2018)Space Allocation in the Retail Industry: A Decision Support System Integrating Evolutionary Algorithms and Regression Models., and . ECML/PKDD (3), volume 8190 of Lecture Notes in Computer Science, page 531-546. Springer, (2013)Towards Automatic Generation of Metafeatures., , and . PAKDD (1), volume 9651 of Lecture Notes in Computer Science, page 215-226. Springer, (2016)Combining regression models and metaheuristics to optimize space allocation in the retail industry., , and . Intell. Data Anal., 19 (s1): S149-S162 (2015)An Empirical Methodology to Analyze the Behavior of Bagging., , and . ADMA, volume 8933 of Lecture Notes in Computer Science, page 199-212. Springer, (2014)Arbitrage of forecasting experts., , , and . Mach. Learn., 108 (6): 913-944 (2019)Automatic Model Monitoring for Data Streams., , and . CoRR, (2019)