用熵加权的概率空间能更好选出有代表性的样本用于主动学习。
Diversity-Based Active Learning: An Evaluation of Metric Spaces for Active Learning Selection
- 将数据映射到模型输出的概率空间并加权熵值来选样本。
- 在合成与真实数据集上，该方法显著优于其他空间选择方式。
- 适合想降低标注成本又追求模型泛化性能的研究者。
近年来，分类模型发展迅速，但训练需大量标注数据。主动学习通过仅查询最具信息量的样本降低标注成本。池基主动学习中，基于多样性的方法旨在选取代表性子集。现有目标包括精确的K中心、精确的K中位数和贪心K中心。本文评估了贪心K中心在多种度量空间中的表现：原始特征空间、线性判别分析（LDA）空间，以及模型生成的概率空间（含与不含熵加权）。以随机森林为基准分类器，在合成与真实数据集上的实证结果表明，将未标注样本映射至预测概率空间并按熵加权，通常显著优于其他选择策略。
原文摘要 · Abstract (English)
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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