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We propose two general and falsifiable hypotheses about expectations ongeneralization error when learning in the context of concept drift. One positsthat as drift rate increases, the forgetting rate that minimizes generalizationerror will also increase and vice versa. The other posits that as a learner'sforgetting rate increases, the bias/variance profile that minimizesgeneralization error will have lower variance and vice versa. These hypotheseslead to the concept of the sweet path, a path through the 3-d space ofalternative drift rates, forgetting rates and bias/variance profiles on whichgeneralization error will be minimized, such that slow drift is coupled withlow forgetting and low bias, while rapid drift is coupled with fast forgettingand low variance. We present experiments that support the existence of such asweet path. We also demonstrate that simple learners that select appropriateforgetting rates and bias/variance profiles are highly competitive with thestate-of-the-art in incremental learners for concept drift on real-world driftproblems.

On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error