Genetic-Based Synthetic Data Sets for the Analysis of Classifiers Behavior

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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 multiobjective and niching strategies.

Núria Macià, Albert Orriols-Puig, Es

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Genetic Algorithm | HIS 2008 | Information Technology | Specific Complexity | Synthetic Data Sets |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	HIS
Authors	Núria Macià, Albert Orriols-Puig, Ester Bernadó-Mansilla

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Genetic-Based Synthetic Data Sets for the Analysis of Classifiers Behavior

Genetic Algorithm | HIS 2008 | Information Technology | Specific Complexity | Synthetic Data Sets |

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