An online cost sensitive decision-making method in crowdsourcing systems

Abstract

Crowdsourcing has created a variety of opportunities for many challenging problems by leveraging human intelligence. For example, applications such as image tagging, natural language processing, and semantic-based information retrieval can exploit crowd-based human computation to supplement existing computational algorithms. Naturally, human workers in crowdsourcing solve problems based on their knowledge, experience, and perception. It is therefore not clear which problems can be better solved by crowdsourcing than solving solely using traditional machine-based methods. Therefore, a cost sensitive quantitative analysis method is needed.