Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining

Capítulo de libro 2005

José Ramón Cano De Amo, Francisco Herrera Triguero, Manuel Lozano

Páginas 21-39

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Cómo citar esta publicación

Cano De Amo, J. R., Herrera Triguero, F., & Lozano, M. (2005). Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining (A. Ghosh & L. C. Jain, Eds.). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-32358-9_2
BibTeX

Resumen

Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As instance selection can be viewed as a search problem, it could be solved using evolutionary algorithms.

Detalles

Editores
Ashish Ghosh, Lakhmi Jain
DOI
10.1007/3-540-32358-9_2
Editorial
Springer Berlin Heidelberg
Ciudad
Berlin, Heidelberg
ISBN
978-3-540-32358-7