Learning to Sample: an Active Learning Framework
This work addresses the problem of improving active learning efficiency for researchers and practitioners in machine learning, particularly in scenarios with small initial labeled data, representing an incremental advancement in learning-based active learning methods.
The paper tackles the challenge of meta-learning for active learning when labeled data is scarce, proposing the Learning To Sample (LTS) framework that integrates uncertainty and diversity sampling. Experimental results show LTS significantly outperforms baselines with limited label budgets, especially on imbalanced datasets, and effectively addresses cold start issues.
Meta-learning algorithms for active learning are emerging as a promising paradigm for learning the ``best'' active learning strategy. However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning. This is contrary to the nature of active learning which typically starts with a small number of labeled samples. The unavailability of large amounts of labeled samples for training meta-learning models would inevitably lead to poor performance (e.g., instabilities and overfitting). In our paper, we tackle these issues by proposing a novel learning-based active learning framework, called Learning To Sample (LTS). This framework has two key components: a sampling model and a boosting model, which can mutually learn from each other in iterations to improve the performance of each other. Within this framework, the sampling model incorporates uncertainty sampling and diversity sampling into a unified process for optimization, enabling us to actively select the most representative and informative samples based on an optimized integration of uncertainty and diversity. To evaluate the effectiveness of the LTS framework, we have conducted extensive experiments on three different classification tasks: image classification, salary level prediction, and entity resolution. The experimental results show that our LTS framework significantly outperforms all the baselines when the label budget is limited, especially for datasets with highly imbalanced classes. In addition to this, our LTS framework can effectively tackle the cold start problem occurring in many existing active learning approaches.