This document is an abstract for a paper that presents two new techniques for pruning rules generated by rule induction algorithms: 1) A hybrid pruning technique that combines the strengths of separate pruning methods, and 2) An incremental postpruning technique that prunes rules based on a misclassification tolerance threshold. An empirical evaluation found that these pruning techniques improved the accuracy and speed of the RULES-7 rule induction algorithm compared to other state-of-the-art classification techniques.

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SIMPLE HYBRID AND INCREMENTAL POSTPRUNING TECHNIQUES
FOR RULE INDUCTION
ABSTRACT:
Pruning achieves the dual goal of reducing the complexity of the final hypothesis for improved
comprehensibility, and improving its predictive accuracy by minimizing the overfitting due to
noisy data. This paper presents a new hybrid pruning technique for rule induction, as well as an
incremental postpruning technique based on a misclassification tolerance. Although both have
been designed for RULES-7, the latter is also applicable to any rule induction algorithm in
general. A thorough empirical evaluation reveals that the proposed techniques enable RULES-7
to outperform other state-of-the-art classification techniques. The improved classifier is also
more accurate and up to two orders of magnitude faster than before.