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Diversity of Ensembles for Data Stream Classification
[article]
2019
arXiv
pre-print
When constructing a classifier ensemble, diversity among the base classifiers is one of the important characteristics. Several studies have been made in the context of standard static data, in particular, when analyzing the relationship between a high ensemble predictive performance and the diversity of its components. Besides, ensembles of learning machines have been performed to learn in the presence of concept drift and adapt to it. However, diversity measures have not received much research
arXiv:1902.08466v1
fatcat:pa5eqgdwt5a5libyeidvgm6qdm