Document Type

Article

Author ORCID Identifier

Yagnashree Velanki https://orcid.org/0009-0009-9460-3286

Pouya Hosseinzadeh https://orcid.org/0000-0001-8045-2709

Soukaina Filali Boubrahimi https://orcid.org/0000-0001-5693-6383

Shah Muhammad Hamdi https://orcid.org/0000-0002-9303-7835

Journal/Book Title/Conference

Universe

Volume

10

Issue

9

Publisher

MDPI AG

Publication Date

9-19-2024

Journal Article Version

Version of Record

First Page

1

Last Page

17

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple methods, our ensemble method provides a more comprehensive understanding of the importance of features. Our results show that the ensemble approach significantly improves classification performance, achieving values 0.15 higher in True Skill Statistic (TSS) values compared to individual feature selection methods. Additionally, our method offers valuable insights into the underlying physical processes of solar flares, leading to more effective space weather forecasting and enhanced mitigation strategies for communication, navigation, and power system disruptions.

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