The authors propose a method for learning the dynamics of the decision boundary to maintain classification performance without additional labeled data.
We propose a method for learning the dynamics of the decision boundary to maintain classification performance without additional labeled data. In various applications, such as spam-mail classification, the decision boundary dynamically changes over time. Accordingly, the performance of classifiers deteriorates quickly unless the classifiers are retrained using additional labeled data. However, continuously preparing such data is quite expensive or impossible. The proposed method alleviates this deterioration in performance by using newly obtained unlabeled data, which are easy to prepare, as well as labeled data collected beforehand. With the proposed method, the dynamics of the decision boundary is modeled by Gaussian processes. To exploit information on the decision boundaries from unlabeled data, the low-density separation criterion, i.e., the decision boundary should not cross high-density regions, but instead lie in low-density regions, is assumed with the proposed method. We incorporate this criterion into our framework in a principled manner by introducing the entropy posterior regularization to the posterior of the classifier parameters on the basis of the generic regularized Bayesian framework. We developed an efficient inference algorithm for the model based on variational Bayesian inference. The effectiveness of the proposed method was demonstrated through experiments using two synthetic and four real-world data sets.