Active Feature Selection for the Mutual Information Criterion

TL;DR

This paper introduces an active feature selection algorithm that efficiently identifies the most informative features using limited labels, leveraging multi-armed bandit insights to outperform naive methods.

Contribution

The paper presents a novel active feature selection method based on mutual information, integrating bandit strategies to reduce label requirements while maintaining high feature quality.

Findings

The proposed algorithm outperforms naive approaches in experiments.

It effectively reduces label usage while selecting high mutual information features.

The methodology can be adapted to other feature quality measures.

Abstract

We study active feature selection, a novel feature selection setting in which unlabeled data is available, but the budget for labels is limited, and the examples to label can be actively selected by the algorithm. We focus on feature selection using the classical mutual information criterion, which selects the $k$ features with the largest mutual information with the label. In the active feature selection setting, the goal is to use significantly fewer labels than the data set size and still find $k$ features whose mutual information with the label based on the \emph{entire} data set is large. We explain and experimentally study the choices that we make in the algorithm, and show that they lead to a successful algorithm, compared to other more naive approaches. Our design draws on insights which relate the problem of active feature selection to the study of pure-exploration multi-armed bandits settings. While we focus here on mutual information, our general methodology can be adapted to other feature-quality measures as well. The code is available at the following url: https://github.com/ShacharSchnapp/ActiveFeatureSelection.