On weighted uncertainty sampling in active learning

2019/09/11 by Vinay Jethava, Jethava, Vinay
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms

paper · pdf · doi:10.48550/arxiv.1909.04928

openalex publication_date 2019/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This note explores probabilistic sampling weighted by uncertainty in active learning. This method has been previously used and authors have tangentially remarked on its efficacy. The scheme has several benefits: (1) it is computationally cheap, (2) it can be implemented in a single-pass streaming fashion which is a benefit when deployed in real-world systems where different subsystems perform the suggestion scoring and extraction of user feedback, and (3) it is easily parameterizable. In this paper, we show on publicly available datasets that using probabilistic weighting is often beneficial and strikes a good compromise between exploration and representation especially when the starting set of labelled points is biased.

Citations

• An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

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