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Hence, this paper presents an empirical analysis of Dynamic Selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and 14 Dynamic Selection schemes. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the AUC and the [Formula: see text]-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases. <\/jats:p>","DOI":"10.1142\/s0218001419400093","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T04:48:52Z","timestamp":1545886132000},"page":"1940009","source":"Crossref","is-referenced-by-count":18,"title":["Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning"],"prefix":"10.1142","volume":"33","author":[{"given":"Rafael M. 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