Obtaining Pareto Front in Instance Selection with Ensembles and Populations

Collective computational intelligence can be used in several ways, for example as taking the decision together by some form of a bagging ensemble or as finding the solutions by multi-objective evolutionary algorithms. In this paper we examine and compare the application of the two approaches to instance selection for creating the Pareto front of the selected subsets, where the two objectives are classification accuracy and data size reduction. As the bagging ensemble members we use DROP5 algorithms. The evolutionary algorithm is based on NSGA-II. The findings are that the evolutionary approach is faster (contrary to the popular belief) and usually provides better quality solutions, with some exceptions, were the outcome of the DROP5 ensemble is better.

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