Naive Bayes Classifier for Positive Unlabeled Learning with Uncertainty
Jiazhen He, Yang Zhang, Xue Li, Yong Wang · 2010
Existing algorithms for positive unlabeled learning (PU learning) only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. In this paper, based on positive naive Bayes (PNB), which is a PU learning algorithm for certain data, we propose an algorithm to handle uncertain data. However, it requires the prior probability of positive class and in real-life applications it is generally difficult for the users to provide this parameter, which is a drawback inherited from traditional PNB algorithm. We improve it by selecting the value of the prior probability of positive class automatically that can make the obtained classifier achieved optimal performance on the validation set. The conducted experiments show that the proposed algorithm yields good performance without user-specified the prior probability of positive class and has satisfactory performance even on highly uncertain data.