Active Learning Using Uncertainty Information

TL;DR

This paper introduces a novel active learning approach that leverages uncertainty information to improve retraining-based models, reducing human labeling efforts across various real-world datasets.

Contribution

It proposes a new method that uses uncertainty information to enhance retraining-based active learning algorithms, demonstrating improved performance.

Findings

Effective in reducing labeling efforts

Improves performance of existing algorithms

Validated on diverse real-world datasets

Abstract

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base our selection on. However, since the true label of the selected instance is unknown, these methods resort to calculating the average-case or worse-case performance with respect to the unknown label. In this paper, we propose a different method to solve this problem. In particular, our method aims to make use of the uncertainty information to enhance the performance of retraining-based models. We apply our method to two state-of-the-art algorithms and carry out extensive experiments on a wide variety of real-world datasets. The results clearly demonstrate the effectiveness of the proposed method and indicate it can reduce human labeling efforts in many real-life applications.