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On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining (2006)
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| Venue: | Applied Soft Computing |
| Citations: | 19 - 0 self |
BibTeX
@ARTICLE{Cano06onthe,
author = {Jose Ramon Cano and Francisco Herrera and Manuel Lozano},
title = {On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining},
journal = {Applied Soft Computing},
year = {2006},
volume = {2006},
pages = {323--332}
}
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Abstract
Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As Training Set Selection can be viewed as a search problem, it could be solved using evolutionary algorithms. In this paper, we have carried out an empirical study of the performance of CHC as representative evolutionary algorithm model. This study includes a comparison between this algorithm and other non-evolutionary instance selection algorithms applied in large size data sets for Training Set Selection. The results show that the stratified evolutionary instance selection algorithms consistently outperform the non-evolutionary ones, the main advantages being: better instance reduction rates, higher classification accuracy and models that are easier to interpret. 1
Keyphrases
evolutionary algorithm data mining stratified strategy training set selection main advantage stratified evolutionary instance selection classification accuracy adaptive method search problem instance reduction rate empirical study representative evolutionary algorithm model non-evolutionary instance selection natural evolution large size data set non-evolutionary one
