Optimal Bayesian classification in nonstationary streaming environments

A novel method of classifying data drawn from a nonstationary distribution with drifting mean and variance is presented. The novelty of the approach is based on splitting the problem of tracking a nonstationary distribution into separate classification and time series state estimation problems. State space models for drift in both the mean and variance are presented, which are then successfully tracked using a Kaiman filter and a particle filter for the linear and non-linear parts respectively. Preliminary results, which show the promising potential of the approach, are also presented, along with concluding remarks for potential uses of the proposed approach.