Variational inference based bayes online classifiers with concept drift adaptation. (September 2018) - Nguyen, Thi Thu Thuy; Nguyen, Tien Thanh; Liew, Alan Wee-Chung; Wang, Shi-Lin
Highlights: A novel online classifier based on Bayes variational inference is proposed. Two new concept drift adaptation techniques are proposed for the online classifier. State-of-the-art performance is achieved for the proposed method. 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.