Active Learning Using Uncertainty Information
This work addresses the challenge of reducing labeling costs in machine learning applications, though it appears incremental as it builds on existing state-of-the-art algorithms.
The paper tackles the problem of selecting unlabeled instances in active learning by proposing a method that uses uncertainty information to improve retraining-based models, demonstrating effectiveness across various real-world datasets and reducing human labeling efforts.
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.