Migratory logistic regression for learning concept drift between two data sets with application to UXO sensing

To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper, we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples drawn from two different distributions T and A (called concepts, borrowing a term from psychology), where Da are fully labeled and Dp partially labeled, our objective is to complete the labels of Dp. We introduce an auxiliary variable μ for each example in Da to reflect its mismatch with Dp. Under an appropriate constraint the μs are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in Dp. The proposed algorithm, called migratory logistic regression, is demonstrated successfully on simulated data as well as on real measured data of interest for unexploded ordnance cleanup. © 2006 IEEE.