1. 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.
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