We consider the M-ary classification problem via crowdsourcing, where crowd workers respond to simple binary questions and the answers are aggregated via decision fusion. The workers have a reject option to skip answering a question when they do not have the expertise, or when the confidence of answering that question correctly is low. We further consider that there are spammers in the crowd who respond to the questions with random guesses. Under the payment mechanism that encourages the reject option, we study the behavior of honest workers and spammers, whose objectives are to maximize their monetary rewards. To accurately characterize human behavioral aspects, we employ prospect theory to model the rationality of the crowd workers, whose perception of costs and probabilities are distorted based on some value and weight functions, respectively. Moreover, we estimate the number of spammers and employ a weighted majority voting decision rule, where we assign an optimal weight for every worker to maximize the system performance. The probability of correct classification and asymptotic system performance are derived. We also provide simulation results to demonstrate the effectiveness of our approach.

中文翻译：

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基于前景理论的众包在存在垃圾邮件发送者的情况下进行分类


我们通过众包考虑 M 元分类问题，其中众包工作人员回答简单的二元问题，并通过决策融合来聚合答案。当工作人员不具备专业知识或正确回答问题的信心较低时，他们可以选择跳过回答问题。我们进一步认为人群中存在垃圾邮件发送者，他们用随机猜测来回答问题。在鼓励拒绝选项的支付机制下，我们研究了诚实工人和垃圾邮件发送者的行为，他们的目标是最大化他们的金钱奖励。为了准确地表征人类行为方面，我们采用前景理论来模拟群众工作者的理性，他们对成本和概率的感知分别基于某些价值和权重函数而被扭曲。此外，我们估计垃圾邮件发送者的数量，并采用加权多数投票决策规则，为每个工作人员分配最佳权重，以最大限度地提高系统性能。推导出正确分类的概率和渐近系统性能。我们还提供模拟结果来证明我们方法的有效性。