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Published in:
2010 | OriginalPaper | Chapter
Scaling Up the Accuracy of Bayesian Classifier Based on Frequent Itemsets by M-estimate
Authors : Jing Duan, Zhengkui Lin, Weiguo Yi, Mingyu Lu
Published in: Artificial Intelligence and Computational Intelligence
Publisher: Springer Berlin Heidelberg
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Frequent Itemsets Mining Classifier (FISC) is an improved Bayesian classifier which averaging all classifiers built by frequent itemsets. Considering that in learning Bayesian network classifier, estimating probabilities from a given set of training examples is crucial, and it has been proved that m-estimate can scale up the accuracy of many Bayesian classifiers. Thus, a natural question is whether FISC with m-estimate can perform even better. Response to this problem, in this paper, we aim to scale up the accuracy of FISC by m-estimate and propose new probability estimation formulas. The experimental results show that the Laplace estimate used in the original FISC performs not very well and our m-estimate can greatly scale up the accuracy, it even outperforms other outstanding Bayesian classifiers used to compare.