The Occam's Razor principle has become the basis for many Machine Learning algorithms, under the interpretation that the classifier should not be more complex than necessary. Recently, this principle has shown to be well suited to associative classifiers, where the number of rules composing the classifier can be substantially reduced by using condensed representations such as maximal or closed rules. While it is shown that such a decrease in the complexity of the classifier (usually) does not

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... mpromise its accuracy, the number of remaining rules is still larger than necessary and making it hard for experts to interpret the corresponding classifier. In this paper we propose a much more aggressive filtering strategy, which decreases the number of rules within the classifier dramatically without hurting its accuracy. Our strategy consists in evaluating each rule under different statistical criteria, and filtering only those rules that show a positive balance between all the criteria considered. Specifically, each candidate rule is associated with a point in an n-dimensional scattergram, where each coordinate corresponds to a statistical criterion. Points that are not dominated by any other point in the scattergram compose the Pareto frontier, and correspond to rules that are optimal in the sense that there is no rule that is better off when all the criteria are taken into account. Finally, rules lying in the Pareto frontier are filtered and compose the classifier. Our Pareto-Optimal filtering strategy may receive as input either the entire set of rules or even a condensed representation (i.e., closed rules). A systematic set of experiments involving benchmark data as well as recent data from actual application scenarios, followed by an extensive set of significance tests, reveal that the proposed strategy decreases the number of rules by up to two orders of magnitude and produces classifiers that are extremely readable (i.e., allow interpretability of the classification results) without hurting accuracy.