Hierarchical Subquery Evaluation for Active Learning on a Graph
This work addresses the variability in active learning for training classifiers, making it more practical for human experts by reducing wasted time, though it appears incremental as it builds on Expected Error Reduction.
The paper tackled the problem of inconsistent and inefficient active learning strategies by proposing a new hierarchical subquery evaluation algorithm with perplexity-based graph construction, achieving high accuracy and consistency while matching human expert time budgets.
To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.