Hierarchical Subquery Evaluation for Active Learning on a Graph

TL;DR

This paper introduces a hierarchical subquery evaluation algorithm combined with perplexity-based graph construction to improve active learning efficiency and consistency, especially for large datasets, by better utilizing Expected Error Reduction.

Contribution

It presents a novel hierarchical subquery evaluation method that enhances active learning performance and consistency, making Expected Error Reduction more practical for large-scale datasets.

Findings

Algorithm achieves high accuracy across diverse datasets

Consistent performance over multiple runs

Queries labels efficiently matching human time budgets

Abstract

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.