BBAuthors: Zhu, Y.; Advisor: -; Participants: Yang, K. (2019)
Most existing approaches to anomaly detection focus on statistical features of the data. However, in many cases, users are merely interested in a subset of the statistical outliers depending on the speci c domain of interest, e.g., network attacks or nancial fraud. The instruction from human experts is therefore indispensable in building predictive models in such applications. However, obtaining labels from human experts is time-consuming and expensive. Obtaining labels from nonexpert labelers are relatively easy and cost-effective. However, the labeling accuracy of a nonexpert is usually dif cult to assess. Therefore, it remains open to leverage both the machine intelligence and the... 
