A Comparison of Methods for Rule Subset Selection Applied to Associative Classification.
Gustavo E. A. P. A. Batista, Cláudia Regina Milaré, Ronaldo Cristiano Prati, Maria Carolina Monard · INTELIGENCIA ARTIFICIAL · 2006
"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."