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Simple Hybrid and Incremental Postpruning Techniques for Rule Induction
Feb. 2013 (vol. 25 no. 2)
pp. 476-480
K. Shehzad, University of Engineering and Technology, Taxila, Pakistan
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.
Index Terms:
Accuracy,Noise measurement,Classification algorithms,Training data,Noise,Runtime,Earth,classification,Overfitting,pruning,noise handling,data mining,knowledge discovery,machine learning,inductive learning,supervised learning,rule induction
Citation:
K. Shehzad, "Simple Hybrid and Incremental Postpruning Techniques for Rule Induction," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 2, pp. 476-480, Feb. 2013, doi:10.1109/TKDE.2011.237
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