Adapting classification rule induction to subgroup discovery Nada Lavrac, Peter A. Flach, Branko Kavsek, and Ljupco Todorovski   View Paper (PDF)   Return to Main-Track Regular Papers Abstract le learning is typically used for solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. This paper shows how this can be achieved by modifying the covering algorithm and the search heuristic, performing probabilistic classification of instances, and using an appropriate measure for evaluating the results of subgroup discovery. Experimental evaluation of the CN2-SD subgroup discovery algorithm on 17 UCI data sets demonstrates substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under the ROC curve. DiSC'03 © 2003 Association for Computing Machinery