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| Format: | Artículo científico |
| Language: | en |
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Asociación Española para la Inteligencia Artificial
2006
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| Online Access: | https://www.redalyc.org/articulo.oa?id=92503205 |
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| _version_ | 1866568996037328896 |
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| author | Gustavo E. A. P. A. Batista |
| author_facet | Gustavo E. A. P. A. Batista |
| contents | A Comparison of Methods for Rule Subset Selection Applied to Associative Classification Gustavo E. A. P. A. Batista Claudia R. Milaré Ronaldo C. Prati Maria C. Monard Ingeniería Machine Learning Genetic Algorithms Rule Subset Selection Associative Classification This paper presents Garss, a new algorithm for rule subset selection based on genetic algorithms, whichuses the area under the ROC curve AUC as fitness function. Garss is a post-processing methodthat can be applied to any rule learning algorithm. In this work, Garss is analysed in the context ofassociative classification, where an association rule algorithm generates a set rules to be used as a classifier.An experimental evaluation was performed in order to analyse the behaviour of the proposed method. Resultsare compared with Roccer, a recently proposed algorithm for rule subset selection based on ROC analysis. 2006 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503205 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.32 Vol.10 |
| format | Artículo científico |
| id | redalyc_92503205 |
| language | en |
| publishDate | 2006 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | A Comparison of Methods for Rule Subset Selection Applied to Associative Classification Gustavo E. A. P. A. Batista Ingeniería Machine Learning Genetic Algorithms Rule Subset Selection Associative Classification A Comparison of Methods for Rule Subset Selection Applied to Associative Classification Gustavo E. A. P. A. Batista Claudia R. Milaré Ronaldo C. Prati Maria C. Monard Ingeniería Machine Learning Genetic Algorithms Rule Subset Selection Associative Classification This paper presents Garss, a new algorithm for rule subset selection based on genetic algorithms, whichuses the area under the ROC curve AUC as fitness function. Garss is a post-processing methodthat can be applied to any rule learning algorithm. In this work, Garss is analysed in the context ofassociative classification, where an association rule algorithm generates a set rules to be used as a classifier.An experimental evaluation was performed in order to analyse the behaviour of the proposed method. Resultsare compared with Roccer, a recently proposed algorithm for rule subset selection based on ROC analysis. 2006 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503205 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.32 Vol.10 |
| title | A Comparison of Methods for Rule Subset Selection Applied to Associative Classification |
| topic | Ingeniería Machine Learning Genetic Algorithms Rule Subset Selection Associative Classification |
| url | https://www.redalyc.org/articulo.oa?id=92503205 |