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

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.

• Type: Conference
• Year: 2008
• Where: HIS
• Authors: Núria Macià, Albert Orriols-Puig, Ester Bernadó-Mansilla
• Keywords: Genetic Algorithm, HIS 2008, Information Technology, Specific Complexity, Synthetic Data Sets