Abstract
Evolutionary algorithms has been recently used for prototype selection showing good results. An important problem in prototype selection consist in increasing the size of data sets. This problem can be harmful in evolutionary algorithms by deteriorating the convergence and increasing the time complexity. In this paper, we offer a preliminary proposal to solve these drawbacks. We propose an evolutionary algorithm that incorporates knowledge about the prototype selection problem. This study includes a comparison between our proposal and other evolutionary and non-evolutionary prototype selection algorithms. The results show that incorporating knowledge improves the performance of evolutionary algorithms and considerably reduces time execution.
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García, S., Cano, J.R., Herrera, F. (2006). Incorporating Knowledge in Evolutionary Prototype Selection. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_161
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DOI: https://doi.org/10.1007/11875581_161
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