A Novel Feature Selection Method for Solar Flare Forecasting

dc.contributor.authorShenoy, A.N.
dc.contributor.authorVijayasenan, D.
dc.contributor.authorBobbi, R.S.
dc.contributor.authorPadinhatteeri, S.
dc.contributor.authorAdithya, H.N.
dc.date.accessioned2026-02-06T06:33:56Z
dc.date.issued2024
dc.description.abstractLarge solar flares (SFs) can disrupt radio communication and harm instruments and astronauts. Hence, it's crucial to predict SFs. However, the mechanism that triggers SFs is not yet known. We only have several physical features believed to be related to the process. This makes choosing the most impactful features for SF production important. We investigate a feature selection method based on the weights learned by a linear classifier. We use the Spaceweather HMI Active Region Patch (SHARP) summary parameters based on the Solar Dynamics Observatory's Helioseismic and Magnetic Imager data records. The records are from May 2010 to December 2019. © 2024 IEEE.
dc.identifier.citation2024 IEEE Space, Aerospace and Defence Conference, SPACE 2024, 2024, Vol., , p. 656-659
dc.identifier.urihttps://doi.org/10.1109/SPACE63117.2024.10668098
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/28937
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectfeature selection
dc.subjectSHARP summary parameters
dc.subjectSolar flare
dc.titleA Novel Feature Selection Method for Solar Flare Forecasting

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