2017/10/26 by Qunwei Li, Li, Qunwei, Pramod K. Varshney +1
Business, Management and Accounting · Computer Science · Decision Sciences · #Auction Theory and Applications #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Social and Information Networks (cs.SI) #Supply Chain and Inventory Management
paper · pdf · doi:10.48550/arxiv.1710.09901
openalex publication_date 2017/10/26 · openalex created_date 2017/11/10 · openalex updated_date 2026/07/28
We explore the design of an effective crowdsourcing system for an M-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance.