The AdPreqFr4SL learning framework for Bayesian Network Classiﬁers is designed to handle the cost / performance 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 Naive Bayes, we scale up the complexity by gradually updating attributes and structure. 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.
Project homepage: http://adpreqfr4sl.sourceforge.net
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