Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing

2015/07/26 by Hao Zhang, Masashi Sugiyama, Zhang, Hao +1 · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Mobile Crowdsensing and Crowdsourcing

paper · pdf · doi:10.48550/arxiv.1507.07199

openalex publication_date 2015/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Task selection (picking an appropriate labeling task) and worker selection (assigning the labeling task to a suitable worker) are two major challenges in task assignment for crowdsourcing. Recently, worker selection has been successfully addressed by the bandit-based task assignment (BBTA) method, while task selection has not been thoroughly investigated yet. In this paper, we experimentally compare several task selection strategies borrowed from active learning literature, and show that the least confidence strategy significantly improves the performance of task assignment in crowdsourcing.

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