LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Human-Centered Information Fusion: Artech House Electronic Warfare Library
Human-Centered Information Fusion: Artech House Electronic Warfare Library
The Journal of Machine Learning Research
Location-based crowdsourcing: extending crowdsourcing to the real world
Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
Combining human and machine intelligence in large-scale crowdsourcing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Crowd IQ: aggregating opinions to boost performance
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the users's trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower local- isation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions.