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A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency

A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency

Salvador García, José Ramón Cano, Francisco Herrera
Copyright: © 2010 |Pages: 22
ISBN13: 9781605667980|ISBN10: 1605667986|ISBN13 Softcover: 9781616924164|EISBN13: 9781605667997
DOI: 10.4018/978-1-60566-798-0.ch005
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MLA

García, Salvador, et al. "A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency." Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, edited by Raymond Chiong, IGI Global, 2010, pp. 92-113. https://doi.org/10.4018/978-1-60566-798-0.ch005

APA

García, S., Cano, J. R., & Herrera, F. (2010). A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency. In R. Chiong (Ed.), Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications (pp. 92-113). IGI Global. https://doi.org/10.4018/978-1-60566-798-0.ch005

Chicago

García, Salvador, José Ramón Cano, and Francisco Herrera. "A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency." In Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, edited by Raymond Chiong, 92-113. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-798-0.ch005

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Abstract

Evolutionary algorithms have been successfully used in different data mining problems. Given that the prototype selection problem could be seen as a combinatorial problem, evolutionary algorithms have been used to solve it with promising results. This chapter presents an evolutionary data mining application known as evolutionary prototype selection. Various approaches have been proposed in the literature following two strategies on the use of evolutionary algorithms: general evolutionary models and models specific to prototype selection problem. In this chapter, the authors review the representative evolutionary prototype selection algorithms proposed, give their description and analyze their performance in terms of efficiency and effectiveness. They study their performance considering different sizes of the data sets, and analyze their behavior when the database scales up. The results are statistically contrasted in order to argue the benefits and drawbacks of each model.

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