Data mining with scatter search

Authors:
I. J. García del Amo;M. García Torres;B. Melián Batista;J. A. Moreno Pérez;J. M. Moreno Vega;Raquel Rivero Martín
Affiliations:
Dep. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, Spain;Dep. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, Spain;Dep. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, Spain;Dep. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, Spain;Dep. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, Spain;Dep. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, Spain
Venue:
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Year:
2005

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Abstract

Most Data Mining tasks are performed by the application of Machine Learning techniques. Metaheuristic approaches are becoming very useful for designing efficient tools in Machine Learning. Metaheuristics are general strategies to design efficient heuristic procedures. Scatter Search is a recent metaheuristic that has been successfully applied to solve standard problems in three central paradigms of Machine Learning: Clustering, Classification and Feature Selection. We describe the main components of the Scatter Search metaheuristic and the characteristics of the specific designs to be applied to solve standard problems in these tasks.