Budgeted Learning of Naive-Bayes Classifiers
This addresses a practical issue for machine learning practitioners where data acquisition is costly, though it is incremental as it builds on active learning methods.
The paper tackles the problem of learning naive-Bayes classifiers under a budget constraint for feature label acquisition, presenting a tractable method that incorporates budget knowledge into decision-making to improve performance over traditional greedy strategies.
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.