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

arXiv: https://arxiv.org/abs/1706.07103 · HTML: https://mlsanity.com/paper/1706.07103/

## Key facts

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

## Built on by (most cited first, 11 of 11)

- [How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events](https://mlsanity.com/paper/2103.07542/) (2021)
- [Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting](https://mlsanity.com/paper/2209.07406/) (2022)
- [All-Clear Flare Prediction Using Interval-based Time Series Classifiers](https://mlsanity.com/paper/2105.01202/) (2021)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets](https://mlsanity.com/paper/2102.08361/) (2021)
- [Bad and good errors: value-weighted skill scores in deep ensemble learning](https://mlsanity.com/paper/2103.02881/) (2021)
- [Improving Solar Flare Prediction by Time Series Outlier Detection](https://mlsanity.com/paper/2206.07197/) (2022)
- [Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods](https://mlsanity.com/paper/2209.13779/) (2022)
- [A review of unsupervised learning in astronomy](https://mlsanity.com/paper/2406.17316/) (2024)
- [Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection](https://mlsanity.com/paper/2202.13611/) (2022)
- [PRESOL: a web-based computational setting for feature-based flare forecasting](https://mlsanity.com/paper/2510.01799/) (2025)

## 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.