Exploiting the High Predictive Power of Multi-class Subgroups

Subgroup discovery aims at finding subsets of a population whose class distribution is significantly different from the overall distribution. A number of multi-class subgroup discovery methods has been previously investigated, proposed and implemented in the CN2-MSD system. When a decision tree learner was applied using the induced subgroups as features, it led to the construction of accurate and compact predictive models, demonstrating the usefulness of the subgroups. In this paper we show that, given a significant, sufficient and diverse set of subgroups, no further learning phase is required to build a good predictive model. Our systematic study bridges the gap between rule learning and decision tree modelling by proposing a method which uses the training information associated with the subgroups to form a simple tree-based probability estimator and ranker, RankFree-MSD, without the need for an additional learning phase. Furthermore, we propose an efficient subgroup pruning algorit...

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Tarek Abudawood, Peter A. Flach
• Keywords: Decision Tree, JMLR 2010, Subgroup Discovery, Subgroups