A Comparison of Methods for Rule Subset Selection Applied to Associative Classification

• Autores: G.E.A.P.A. Batista, C. R. Milare, R. C. Prati, M. C. Monard
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 10, Nº. 32, 2006, págs. 29-35
• Idioma: español
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• Resumen

  • This paper presents GARSS, a new algorithm for rule subset selection based on genetic algorithms, which uses the area under the ROC curve -AUC- as fitness function. GARSS is a post-processing method that can be applied to any rule learning algorithm. In this work, GARSS is analysed in the context of associative 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. Results are compared with ROCCER, a recently proposed algorithm for rule subset selection based on ROC analysis.