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Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification

Jiménez, Manuel; Triguero, Isaac; John, Robert

Authors

Manuel Jiménez

Robert John



Abstract

Citizen Science, traditionally known as the engagement of amateur participants in research, is showing great potential for large-scale processing of data. In areas such as astronomy, biology, or geo-sciences, where emerging technologies generate huge volumes of data, Citizen Science projects enable image classification at a rate not possible to accomplish by experts alone. However, this approach entails the spread of biases and uncertainty in the results, since participants involved are typically non-experts in the problem and hold variable skills. Consequently, the research community tends not to trust Citizen Science outcomes, claiming a generalised lack of accuracy and validation.
We introduce a novel multi-stage approach to handle uncertainty within data labelled by amateurs in Citizen Science projects. Firstly, our method proposes a set of transformations that leverage the uncertainty in amateur classifications. Then, a hybridisation strategy provides the best aggregation of the transformed data for improving the quality and confidence in the results. As a case study, we consider the Galaxy Zoo, a project pursuing the labelling of galaxy images. A limited set of expert classifications allow us to validate the experiments, confirming that our approach is able to greatly boost accuracy and classify more images with respect to the state-of-art.

Journal Article Type Article
Publication Date Dec 7, 2018
Journal Information Sciences
Print ISSN 0020-0255
Publisher Elsevier
Peer Reviewed Peer Reviewed
APA6 Citation Jiménez, M., Triguero, I., & John, R. (2018). Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification. Information Sciences, doi:10.1016/j.ins.2018.12.011
DOI https://doi.org/10.1016/j.ins.2018.12.011
Keywords Control and Systems Engineering; Theoretical Computer Science; Software; Information Systems and Management; Artificial Intelligence; Computer Science Applications
Publisher URL https://www.sciencedirect.com/science/article/pii/S0020025518309538#ack0001

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