Many of the existing classifiers cannot deal with exceptions, which are not to be ignored in real life. In this paper, an exception-tolerant methodology is proposed based on each of the following three popular algorithms for multi-class classification problems: C4.5, PRISM and RISE. The performance of each algorithm was improved by converting the outputs into the format of RISE induced rules, applying bagging and boosting techniques, adding exceptions with a default rule, and excluding inefficient rules. The improved versions of C4.5, PRISM and RISE are named as OE 2 -C4.5, OE 2 -PRISM and OE 2 -RISE, respectively. Note that OE 2 stands for Ordering of Efficient Rules and Inclusion of Exceptions. Empirical results show that OE 2 -C4.5 and OE 2 -RISE significantly outperformed classical C4.5, PRISM and RISE for each dataset. Our methodology provides a reference for improving other weak learners individually or in an ensemble.
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