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Author (up) Zhu, D. doi  openurl
  Title A hybrid approach for efficient ensembles Type
  Year 2010 Publication Decision Support Systems Abbreviated Journal  
  Volume 48 Issue 3 Pages 480-487  
  Keywords Ensembles; Classification; Data Envelopment Analysis (DEA ); Stacking  
  Abstract An ensemble of classifiers, or a systematic combination of individual classifiers, often results in better classifications in comparison to a single classifier. However, the question regarding what classifiers should be chosen for a given situation to construct an optimal ensemble has often been debated. In addition, ensembles are often computationally expensive since they require the execution of multiple classifiers for a single classification task. To address these problems, we propose a hybrid approach for selecting and combining data mining models to construct ensembles by integrating Data Envelopment Analysis and stacking. Experimental results show the efficiency and effectiveness of the proposed approach.  
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  Notes Approved  
  Call Number Admin @ admin @ Zhu2010 Serial 4810  
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