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

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. G ARSS is a post-processing method that can be applied to any rule learning algorithm. In this work, G ARSS is analysed in the context of associa- tive 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 re- cently proposed algorithm for rule subset selection based on ROC analysis.