EMO shines a light on the holes of complexity space

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.