Original Contribution: Stacked generalization
Neural Networks
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
In search of targeted-complexity problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Typical domains used in machine learning analyses only cover the complexity space partially, remaining a large proportion of problem difficulties that are not tested. Since the acquisition of new real-world problems is costly, the machine learning community has started giving importance to the automatic generation of learning domains with bounded difficulty. This paper proposes the use of an evolutionary multi-objective technique to generate artificial data sets that meet specific characteristics and fill these holes. The results show that the multi-objective evolutionary algorithm is able to create data sets of different complexities, covering most of the solution space where we had no real-world problem representatives. The proposed method is the starting point to study data complexity estimates and steps forward in the gap between data and learners.