mlsanityData to 14 Oct 2026

A hybrid supervised/unsupervised machine learning approach to solar flare prediction

arXiv 1706.07103 ↗First posted 21 Jun 2017astro-ph.SR cs.LGcited by 11

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Abstract (arXiv)

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jun 2017.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 1; 2024: 1; 2025: 1.

11 papers.

  1. 2021How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Eventscited by 26
  2. 2022Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecastingcited by 17
  3. 2021All-Clear Flare Prediction Using Interval-based Time Series Classifierscited by 13
  4. 2023Machine learning in solar physicscited by 10
  5. 2021A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasetscited by 5
  6. 2021Bad and good errors: value-weighted skill scores in deep ensemble learningcited by 5
  7. 2022Improving Solar Flare Prediction by Time Series Outlier Detectioncited by 3
  8. 2022Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methodscited by 3
  9. 2024A review of unsupervised learning in astronomycited by 2
  10. 2022Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detectioncited by 1
  11. 2025PRESOL: a web-based computational setting for feature-based flare forecastingcited by 0