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Communications in Computer and Information Science
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimesdoi:10.1007/978-3-319-44188-7_3 fatcat:clewj6mwrvbnlb5sg6cccf75dq