@article{2017-Rosales-Perez-EvolutionaryMultiobjectiveModel, title = {An {{Evolutionary Multiobjective Model}} and {{Instance Selection}} for {{Support Vector Machines With Pareto}}-{{Based Ensembles}}}, volume = {21}, issn = {1089-778X}, doi = {10.1109/TEVC.2017.2688863}, number = {6}, journal = {IEEE Transactions on Evolutionary Computation}, author = {{Rosales-P\'erez}, A. and Garc\'ia, S. and Gonzalez, J. A. and Coello, C. A. Coello and Herrera, F.}, month = dec, year = {2017}, keywords = {classification performance,classification task,Computational efficiency,ensembles,evolutionary computation,Evolutionary computation,evolutionary multiobjective model,filter approach,global Pareto ensemble,high computational cost,hyper-parameters,instance selection,Instance selection (IS),large-scale datasets,learning (artificial intelligence),model selection (MS),multiobjective evolutionary algorithms,multiobjective optimization,Pareto optimal solutions,Pareto optimisation,Pareto optimization,Pareto processing,pattern classification,powerful learning algorithms,Proposals,single ensemble,support vector machines,Support vector machines,support vector machines (SVMs),SVM,Training,training phase,training set}, pages = {863-877} }