Abstract
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.
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This work was presented in part and awarded as Young Author Award at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008
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Kuwajima, I., Nojima, Y. & Ishibuchi, H. Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal rules. Artif Life Robotics 13, 315–319 (2008). https://doi.org/10.1007/s10015-008-0544-2
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DOI: https://doi.org/10.1007/s10015-008-0544-2