Budgeted Learning of Naive-Bayes Classifiers

TL;DR

This paper introduces a budget-aware approach for training Naive Bayes classifiers, optimizing feature acquisition costs during learning to improve performance within financial constraints.

Contribution

It presents a tractable method for integrating budget considerations into feature selection, moving beyond traditional greedy active learning strategies.

Findings

Budget-aware feature acquisition improves classifier performance.

The proposed method outperforms greedy strategies under cost constraints.

Incorporating budget knowledge enhances learning efficiency.

Abstract

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.