Variational inference based bayes online classifiers with concept drift adaptation

Abstract

We present VIGO, a novel online Bayesian classifier for both binary and multiclass problems. In our model, variational inference for multivariate distribution technique is exploited to approximate the class conditional probability density functions of data in an online manner. To handle concept drift that could arise in streaming data, we develop 2 new adaptive methods based on VIGO, which we called VIGOw and VIGOd. While VIGOw naturally adapts to any kind of changing environments, VIGOd maximises the benefit of a static environment as long as it does not detect any change. Extensive experiments on big/medium real-world/synthetic datasets demonstrate the superior performance of our algorithms over many state-of-the-art methods in the literature.