Two-cornered learning classifier systems for pattern generation and classification
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper described an automated pattern generator to generate various synthetic data sets for classification problems, where the problem's complexity can be manipulated autonomously. The Tabu Search technique has been applied in the pattern generator to discover the best combination of domain features in order to adjust the complexity levels of the problem. Experiments confirm that the pattern generator was able to tune the problem's complexity so that it can either increase or decrease the classification performance. The novel contributions in this work enable the effect of domain features that alter classification performance, to become human readable. This work provides a new method for generating artificial datasets at various levels of difficulty where the difficulty levels can be tuned autonomously.