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A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification

Jimenez, Manuel; Torres, Mercedes Torres; John, Robert; Triguero, Isaac


Manuel Jimenez

Mercedes Torres Torres

Robert John

Isaac Triguero


Citizen science is becoming mainstream in a wide variety of real-world applications in astronomy or bioinformatics, in which, for example, classification tasks by experts are very time consuming. These projects engage amateur volunteers that are tasked to manually classify unannotated examples. As a result, we obtain a larger volume of labelled data that, however, contains a great level of uncertainty due to the wide range of expertise of the volunteers. Handling that inherent uncertainty is key to building robust and fast machine learning models that maximise the outcome of citizen science projects. In this work, we introduce a preliminary approach that first transforms the original results from a citizen science project to handle the uncertainty, and then uses this as input to a fuzzy k-nearest neighbour classifier. We leverage citizen science results in such a way that it naturally speeds up the learning and classification phases of the fuzzy classifier, and improves the classification performance. As a case study, we will focus on the Galaxy Zoo project that consisted of galaxy image classification. Our experimental results show that an appropriate use of citizen science data enables a faster and more robust classification using the fuzzy k-nearest neighbour classifier.

Start Date Jun 23, 2019
Publication Date 2019-06
Publisher Institute of Electrical and Electronics Engineers
Book Title 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN 9781538617281
APA6 Citation Jimenez, M., Torres, M. T., John, R., & Triguero, I. (2019). A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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