A new MBBCTree classification algorithm based on active learning

Abstract

MBBCTree algorithm, which integrates the advantage of Markov Blanket Bayesian Networks (MBBC) and Decision Tree, performances better than other Bayesian Networks for classification. But MBBCTree classifier was built by the traditional passive learning. The available training samples with actual classes are not enough for passive learning method for modelling MBBCTree classifier in practice. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. In this paper, a new MBBCTree classifier algorithm based on active learning is present to solve the problem of building MBBCTree classifier from unlabelled samples. Experimental results show that the proposed algorithm can reach the same accuracy as passive learning with few labeled training examples.