Probabilistic model for truth discovery with mean and median check framework

作者：

Highlights：

• Addressing the truth discovery problem without following the generally accepted principle.

• Theoretically verifying the absolute distance between the mean and median value.

• Proposing the framework for truth detection, error claim removal, and iteration-stopping criteria.

• Conducting experiments on three datasets to evaluate the performance.

摘要

•Addressing the truth discovery problem without following the generally accepted principle.•Theoretically verifying the absolute distance between the mean and median value.•Proposing the framework for truth detection, error claim removal, and iteration-stopping criteria.•Conducting experiments on three datasets to evaluate the performance.

论文关键词：Data conflict,Truth discovery,Source reliability,Gaussian distribution,Mean and median check

论文评审过程：Received 5 January 2021, Revised 6 September 2021, Accepted 7 September 2021, Available online 30 September 2021, Version of Record 6 October 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107482