基于前景理论的垃圾邮件分类众包研究
Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers
摘要：
我们认为，通过众包的$M$ -ary分类问题，其中人群工作人员响应简单的二进制问题和答案通过决策融合聚合。当工人不具备专业知识或正确回答问题的信心较低时，他们可以选择拒绝回答问题。我们进一步认为，在人群中有垃圾邮件发送者，他们对问题的回答是随机猜测。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回报。为了准确地描述人类的行为方面，我们采用前景理论建模的合理性的人群工作者，其感知的成本和概率被扭曲的基础上，一些价值和权重函数，分别。此外，我们估计垃圾邮件发送者的数量，并采用加权多数表决决策规则，我们分配给每个工人的最佳权重，以最大限度地提高系统的性能。推导出正确分类的概率和渐进系统性能。我们还提供了仿真结果来证明我们的方法的有效性。
英文摘要：
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.
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