EWSMLFS: Explainable Weighted Stacked Ensemble Machine Learning with  Recursive Feature Selection for Higgs Boson Particle Detection and Classification

Anupama patre,Dr. Dhirendra Kumar Gupta,Dr. Anchit Modi

EWSMLFS: Explainable Weighted Stacked Ensemble Machine Learning with Recursive Feature Selection for Higgs Boson Particle Detection and Classification

Authors

Anupama patre,Dr. Dhirendra Kumar Gupta,Dr. Anchit Modi

Keywords:

Higgs boson particle detection, machine learning, weighted stacked ensemble, recursive feature selection, interpretability, high-energy physics.

Abstract

This research paper addresses the critical task of detecting and classifying Higgs Boson Particles (HBP)in high-energy physics research. We propose an explainable weighted stacked ensemble Machine Learning (ML) approach with Recursive Feature Selection (RFS)

References

Gianotti, F., and T. S. Virdee. "The discovery and measurements of a Higgs boson." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373.2032 (2015): 20140384.

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Published

2024-09-05

How to Cite

Anupama patre,Dr. Dhirendra Kumar Gupta,Dr. Anchit Modi. (2024). EWSMLFS: Explainable Weighted Stacked Ensemble Machine Learning with Recursive Feature Selection for Higgs Boson Particle Detection and Classification. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2333–2345. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4299