Scaling up the accuracy of Bayesian classifier based on frequent itemsets by m-estimate

Authors:
Jing Duan;Zhengkui Lin;Weiguo Yi;Mingyu Lu
Affiliations:
Information Science and Technology, Dalian Maritime University, Dalian, China;Information Science and Technology, Dalian Maritime University, Dalian, China;Information Science and Technology, Dalian Maritime University, Dalian, China;Information Science and Technology, Dalian Maritime University, Dalian, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

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Abstract

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.