  Frequently, acquiring training data has an associated cost. We consider the
situation where the learner may purchase data during training, subject TO a
budget. IN particular, we examine the CASE WHERE each feature label has an
associated cost, AND the total cost OF ALL feature labels acquired during
training must NOT exceed the budget.This paper compares methods FOR choosing
which feature label TO purchase next, given the budget AND the CURRENT belief
state OF naive Bayes model parameters.Whereas active learning has traditionally
focused ON myopic(greedy) strategies FOR query selection, this paper presents a
tractable method FOR incorporating knowledge OF the budget INTO the decision
making process, which improves performance.
