OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking

Glenn Dawson, Robi Polikar

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information with which to train sophisticated models. To address this problem, crowdsourcing has emerged as a popular, inexpensive, and efficient data mining solution for performing distributed label collection. However, crowdsourced annotations are inherently untrustworthy, as the labels are provided by anonymous volunteers who may have varying, unreliable expertise. Worse yet, some participants on commonly used platforms such as Amazon Mechanical Turk may be adversarial, and provide intentionally incorrect label information without the end user's knowledge. We discuss three conventional models of the label generation process, describing their parameterizations and the model-based approaches used to solve them. We then propose OpinionRank, a model-free, interpretable, graph-based spectral algorithm for integrating crowdsourced annotations into reliable labels for performing supervised or semi-supervised learning. Our experiments show that OpinionRank performs favorably when compared against more highly parameterized algorithms. We also show that OpinionRank is scalable to very large datasets and numbers of label sources, and requires considerably fewer computational resources than previous approaches.

    Original languageEnglish (US)
    Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9780738133669
    DOIs
    StatePublished - Jul 18 2021
    Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
    Duration: Jul 18 2021Jul 22 2021

    Publication series

    NameProceedings of the International Joint Conference on Neural Networks
    Volume2021-July

    Conference

    Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
    Country/TerritoryChina
    CityVirtual, Shenzhen
    Period7/18/217/22/21

    All Science Journal Classification (ASJC) codes

    • Software
    • Artificial Intelligence

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