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arXiv · 2608.23461

Diversity-Based Active Learning: An Evaluation of Metric Spaces for Active Learning Selection

Siddharth Chilamkur·Dorit S. Hochbaum
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Abstract

With rapid advancement over the last few years, many different methods are now widely used for classification. However, training these models requires substantial labeled data. Active Learning is a potential solution to this problem. Pool-based active learning minimizes costs by querying only the most informative samples from an unlabeled dataset. Diversity-based approaches, on the other hand, attempt to select a representative subset of the data. There are many different objectives for determining the selection process, including exact K-center, exact K-median, and Greedy K-center. In this paper, we will focus on evaluating the performance of Greedy K-center across a variety of metric spaces: the raw feature space, a Linear Discriminant Analysis (LDA) space, and a model-derived probability space (with and without entropy-based weighting). Using Random Forest classifiers as a baseline evaluator, our empirical results on synthetic and real-world datasets demonstrate that mapping unlabeled instances into a predictive probability space and weighting the result by entropy often dominates the other options for active learning selection with Greedy K-center.

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Siddharth Chilamkur, Dorit S. Hochbaum. 2026-08-24. Diversity-Based Active Learning: An Evaluation of Metric Spaces for Active Learning Selection. https://arxiv.org/abs/2608.23461

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