Aiming at the imprecise and uncertain data and knowledge, this paper proposes a novel prior assumption by the rough set theory. The performance of the classical Bayesian classifier is improved through this study. We applied the operations of approximations to represent the imprecise knowledge accurately, and the concept of approximation quality is first applied in this method. Thus, this paper provides a novel rough set theory based prior probability in classical Bayesian classifier and the corresponding rough set prior Bayesian classifier. And we chose 18 public datasets to evaluate the performance of the proposed model compared with the classical Bayesian classifier and Bayesian classifier with Dirichlet prior assumption. Sufficient experimental results verified the effectiveness of the proposed method. The mainly impacts of our proposed method are: firstly, it provides a novel methodology which combines the rough set theory with the classical probability theory; secondly, it improves the accuracy of prior assumptions; thirdly, it provides an appropriate prior probability to the classical Bayesian classifier which can improve its performance only by improving the accuracy of prior assumption and without any effect to the likelihood probability; fourthly, the proposed method provides a novel and effective method to deal with the imprecise and uncertain data; last but not least, this methodology can be extended and applied to other concepts of classical probability theory, which providing a novel methodology to the probability theory.
Distributed and self-organizing data management strategies for wireless sensor networks
