Title: Quantitative CBA: Small and Comprehensible Association Rule Classification Models,
Abstract: Quantitative CBA is a postprocessing algorithm for association rule
classification algorithm CBA (Liu et al, 1998). QCBA uses original,
undiscretized numerical attributes to optimize the discovered association
rules, refining the boundaries of literals in the antecedent of the rules
produced by CBA. Some rules as well as literals from the rules can consequently
be removed, which makes the resulting classifier smaller. One-rule
classification and crisp rules make CBA classification models possibly most
comprehensible among all association rule classification algorithms. These
viable properties are retained by QCBA. The postprocessing is conceptually
fast, because it is performed on a relatively small number of rules that passed
data coverage pruning in CBA. Benchmark of our QCBA approach on 22 UCI datasets
shows average 53% decrease in the total size of the model as measured by the
total number of conditions in all rules. Model accuracy remains on the same
level as for CBA.