Adaptive classification is an important online problem in data analysis. The nonlinear and nonstationary nature of much data makes standard static approaches unsuitable. In this paper, we propose a set of sequential dynamic classification algorithms based on extension of nonlinear variants of Bayesian Kalman processes and dynamic generalized linear models. The approaches are shown to work well not only in their ability to track changes in the underlying decision surfaces but also in their ability to handle in a principled manner missing data. We investigate both situations in which target labels are unobserved and also where incoming sensor data are unavailable. We extend the models to allow for active label requesting for use in situations in which there is a cost associated with such information and hence a fully labelled target set is prohibitive.
['Seung Min Lee', 'Stephen J. Roberts']
Sequential Dynamic Classification Using Latent Variable Models