An adaptive frequential learning framework for bayesian network classifiers

Abstract

We introduce an adaptive prequential learning framework for Bayesian Network Classifiers which attempts to handle the costperformance trade-off and cope with concept drift. Our strategy for incorporating new data is based on bias management and gradual adaptation. Starting with the simple Naïve Bayes, we scale up the complexity by gradually increasing the maximum number of allowable attribute dependencies, and then by searching for new dependences in the extended search space. Since updating the structure is a costly task, we use new data to primarily adapt the parameters and only if this is really necessary, do we adapt the structure. The method for handling concept drift is based on the Shewhart P-Chart. We evaluated our adaptive algorithms on artificial domains and benchmark problems and show its advantages and future applicability in real-world on-line learning systems. © Springer-Verlag Berlin Heidelberg 2006.