Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal rules.

In the field of data mining, confidence and support are often used to measure the quality of a rule. Pareto-optimal rules, which are Pareto-optimal in terms of confidence and support maximization, have an interesting characteristic that Pareto-optimal rules maximize other various rule evaluation criteria. In this paper, we examine the effectiveness of designing classifiers from Pareto-optimal rules. We consider not only Pareto-optimal rules but also near Pareto-optimal rules. To show the effectiveness, we compare classifiers obtained from Pareto-optimal and near Pareto-optimal rules with classifiers obtained from the rules that have large value in terms of other different rule evaluation criteria. Eight criteria are examined in this paper: CF, confidence, cover, Laplace, lift, random, slave, support. Through computational experiments, we show that classifiers obtained from Pareto-optimal rules have higher accuracy than those from rules extracted according to the other criteria. [ABSTRACT FROM AUTHOR]