众包中的正则化Minimax条件熵
Regularized Minimax Conditional Entropy for Crowdsourcing
摘要：
有一个快速增长的兴趣众包数据标签。通过众包，通常可以以低成本快速收集大量标签。然而，众包工作人员提供的标签通常质量不高。在本文中，我们提出了一个最小最大条件熵原则，从嘈杂的众包标签推断地面真相。在这一原则下，我们得到了一个独特的概率标记模型联合工人的能力和项目难度参数。我们还提出了一个客观的测量原则，并表明我们的方法是唯一的方法，满足这一客观测量原则。我们验证我们的方法，通过各种真正的众包数据集与二进制，多类或序数标签。
英文摘要：
There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item difficulty. We also propose an objective measurement principle, and show that our method is the only method which satisfies this objective measurement principle. We validate our method through a variety of real crowdsourcing datasets with binary, multiclass or ordinal labels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
