  We propose two general and falsifiable hypotheses about expectations on
generalization error when learning in the context of concept drift. One posits
that as drift rate increases, the forgetting rate that minimizes generalization
error will also increase and vice versa. The other posits that as a learner's
forgetting rate increases, the bias/variance profile that minimizes
generalization error will have lower variance and vice versa. These hypotheses
lead to the concept of the sweet path, a path through the 3-d space of
alternative drift rates, forgetting rates and bias/variance profiles on which
generalization error will be minimized, such that slow drift is coupled with
low forgetting and low bias, while rapid drift is coupled with fast forgetting
and low variance. We present experiments that support the existence of such a
sweet path. We also demonstrate that simple learners that select appropriate
forgetting rates and bias/variance profiles are highly competitive with the
state-of-the-art in incremental learners for concept drift on real-world drift
problems.
