Abstract
VisIRML is a visual analytic system to classify and display unstructured data. Subject matter experts define topics by iteratively training a machine learning (ML) classifier by labeling of sample articles facilitated via information retrieval (IR) query expansion—i.e. semi-supervised machine learning. The resulting classifier produces high quality labels better than comparable semi-supervised learning techniques. While multiple visualization approaches were considered to depict these articles, users exhibited a strong preference for a map-based representation.
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Hagerman, C., Brath, R., Langevin, S. (2022). VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning Classifier. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Banissi, E. (eds) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1014. Springer, Cham. https://doi.org/10.1007/978-3-030-93119-3_13
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