Learning Balanced Bayesian Classifiers From Labeled and Unlabeled Data

Abstract

How to train learners over unbalanced data with asymmetric costs has been recognized as one of the most significant challenges in data mining. Bayesian network classifier (BNC) provides a powerful probabilistic tool to encode the probabilistic dependencies among random variables in directed acyclic graph (DAG), whereas unbalanced data will result in unbalanced network topology. This will lead to a biased estimate of the conditional or joint probability distribution, and finally a reduction in the classification accuracy. To address this issue, we propose to redefine the information-theoretic metrics to uniformly represent the balanced dependencies between attributes or that between attribute values. Then heuristic search strategy and thresholding operation are introduced to respectively learn refined DAGs from labeled and unlabeled data. The experimental results on 32 benchmark datasets reveal that the proposed highly scalable algorithm is competitive with or superior to a number of state-of-the-art single and ensemble learners.