UCI++: Improved Support for Algorithm Selection Using Datasetoids
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Beyond Homemade Artificial Data Sets
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Combining meta-learning and active selection of datasetoids for algorithm selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Uncertainty sampling-based active selection of datasetoids for meta-learning
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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In this paper, we highlight the use of synthetic data sets to analyze learners behavior under bounded complexity. We propose a method to generate synthetic data sets with a specific complexity, based on the length of the class boundary. We design a genetic algorithm as a search technique and find it useful to obtain class labels according to the desired complexity. The results show the suitability of the genetic algorithm as a framework to provide artificial benchmark problems that can be further enriched with the use of multi-objective and niching strategies.