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In this article, we propose a new ensemble reduction method called\n            <jats:italic>CANOPY<\/jats:italic>\n            that significantly reduces memory storage and computations.\n            <jats:italic>CANOPY<\/jats:italic>\n            uses a technique from logic minimization for digital circuits to select and combine particular classification models from an initial pool in the form of a Boolean function, through which the reduced ensemble performs classification. Experiments on 20 UCI datasets demonstrate that\n            <jats:italic>CANOPY<\/jats:italic>\n            either outperforms or is very competitive with the initial ensemble and one state-of-the-art ensemble reduction method in terms of generalization error, and is superior to all existing reduction methods surveyed for identifying the smallest numbers of models in the reduced ensembles.\n          <\/jats:p>","DOI":"10.1145\/2897515","type":"journal-article","created":{"date-parts":[[2016,5,31]],"date-time":"2016-05-31T12:15:09Z","timestamp":1464696909000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Ensemble Reduction via Logic Minimization"],"prefix":"10.1145","volume":"21","author":[{"given":"Hongfei","family":"Wang","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"R. D. 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