An Ensemble Pruning Primer
作者： Grigorios Tsoumakas , Ioannis Partalas , Ioannis Vlahavas
DOI: 10.1007/978-3-642-03999-7_1
关键词: Machine learning 、 Artificial intelligence 、 Hill climbing 、 Taxonomy (general) 、 Key (cryptography) 、 Pruning (decision trees) 、 Point (typography) 、 Computer science 、 Reduction (complexity)
摘要: Ensemble pruning deals with the reduction of an ensemble predictive models in order to improve its efficiency and performance. The last 12 years a large number methods have been proposed. This work proposes taxonomy for their organization reviews important representative each category. It abstracts key components discusses main advantages disadvantages. We hope that this will serve as good starting point reference researchers working on development new methods.
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