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Combining bagging, boosting, rotation forest and random subspace methods

. Artificial Intelligence Review, (2011)

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Preventing Student Dropout in Distance Learning Using Machine Learning Techniques., , and . KES, volume 2774 of Lecture Notes in Computer Science, page 267-274. Springer, (2003)Bagging Random Trees for Estimation of Tissue Softness., , and . MLDM, volume 3587 of Lecture Notes in Computer Science, page 674-681. Springer, (2005)CST-Voting: A semi-supervised ensemble method for classification problems., , , and . J. Intell. Fuzzy Syst., 35 (1): 99-109 (2018)A Hybrid Decision Support Tool - Using Ensemble of Classifiers., and . ICEIS (2), page 448-456. (2004)Rotation-based model trees for classification.. IJDATS, 2 (1): 22-37 (2010)Towards an ontology-based system for intelligent prediction of firms with fraudulent financial statements., , and . ETFA, page 1300-1307. IEEE, (2007)Integrating Global and Local Voting of Classifiers.. Cybern. Syst., 43 (5): 398-409 (2012)Educational data mining: a case study for predicting dropout-prone students.. Int. J. Knowl. Eng. Soft Data Paradigms, 1 (2): 101-111 (2009)Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades.. Artif. Intell. Rev., 37 (4): 331-344 (2012)Selective costing ensemble for handling imbalanced data sets., and . Int. J. Hybrid Intell. Syst., 6 (3): 123-133 (2009)