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

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. © 2010 Springer-Verlag.