Analysis of Example Weighting in Subgroup Discovery by Comparison of Three Algorithms on a Real-life Data Set

This paper investigates the implications of example weighting in subgroup discovery by comparing three state-of-the-art subgroup discovery algorithms, APRIORI-SD, CN2-SD, and SubgroupMiner on a real-life data set. While both APRIORI-SD and CN2-SD use example weighting in the process of subgroup discovery, SubgroupMiner does not. Moreover, APRIORI-SD uses example weighting in the post-processing step of selecting the ‘best’ rules, while CN2-SD uses example weighting during rule induction. The results of the application of the three subgroup discovery algorithms on a real-life data set – the UK Traffic challenge data set are presented in the form of ROC curves showing that APRIORISD slightly outperforms CN2-SD; both APRIORI-SD and CN2-SD are good in finding small and highly accurate subgroups (describing minority classes), while SubgroupMiner found larger and less accurate subgroups (describing the majority class). We show by using ROC analysis that these results are not surprising and can be attributed to example weighting, that ‘pushes’ the search in the space of potential subgroups towards discovering small and accurate subgroups.