# Online Budgeted Learning for Classifier Induction

## TL;DR

This paper introduces the problem of online budgeted learning for classifier induction, proposing adaptive and random feature acquisition policies based on multi-armed bandits, with adaptive policies generally outperforming random ones.

## Contribution

It formulates the online budgeted learning problem and proposes adaptive and random acquisition policies, demonstrating their effectiveness through experiments on real-world datasets.

## Key findings

- Adaptive policies outperform random policies under most budget constraints.
- Adaptive policies can achieve near-optimal results in some cases.
- Experiments on five real-world datasets validate the proposed framework.

## Abstract

In real-world machine learning applications, there is a cost associated with sampling of different features. Budgeted learning can be used to select which feature-values to acquire from each instance in a dataset, such that the best model is induced under a given constraint. However, this approach is not possible in the domain of online learning since one may not retroactively acquire feature-values from past instances. In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream. In this paper we introduce the issue of online budgeted learning and describe a general framework for addressing this challenge. We propose two types of feature value acquisition policies based on the multi-armed bandit problem: random and adaptive. Adaptive policies perform online adjustments according to new information coming from a data stream, while random policies are not sensitive to the information that arrives from the data stream. Our comparative study on five real-world datasets indicates that adaptive policies outperform random policies for most budget limitations and datasets. Furthermore, we found that in some cases adaptive policies achieve near-optimal results.