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We explore the design of an effective crowdsourcing system for an $M$-aryclassification task. Crowd workers complete simple binary microtasks whoseresults are aggregated to give the final decision. We consider the scenariowhere the workers have a reject option so that they are allowed to skipmicrotasks when they are unable to or choose not to respond to binarymicrotasks. We present an aggregation approach using a weighted majority votingrule, where each worker's response is assigned an optimized weight to maximizecrowd's classification performance.

Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers