The Bayesian logistic regression in pattern recognition problems under concept drift

The practice always makes us face the challenge of processing pattern recognition data flows with time-varying target concept, i.e., changing statistical relationship between class memberships and observable characteristics of entities to be perceived by the recognition system. In this paper, a mathematical and algorithmic framework is proposed for handling the concept drift in pattern recognition problems on the basis of the Bayesian treatment of logistic regression as an appropriate mathematical instrument for inferring a time-varying decision rule. The pattern recognition procedure resulting from this approach is a numerical implementation of the general dynamic programming principle, and has the linear computational complexity with respect to the length of the time series, in contrast to the polynomial complexity of pattern recognition procedures of general kind.