AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning
Grant Stevens, Sotiria Fotopoulou, Malcolm N. Bremer, Oliver Ray
AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field’s immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless
Comments: 7 pages, 4 figures, Journal of Open Source Software
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Journal reference: Journal of Open Source Software (2021), 6(65), 3635
DOI: 10.21105/joss.03635
Cite as: arXiv:2109.05207 [astro-ph.IM] (or arXiv:2109.05207v1 [astro-ph.IM] for this version)
Submission history
From: Grant Stevens
[v1] Sat, 11 Sep 2021 07:32:26 UTC (1,064 KB)
