Boosted decision trees as an alternative to artificial neural networks for particle identification
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Research works printed but not linked: 2
- [9]Byron P. Roe et al., BooNE-TN-117, Mar. 18, 2004. (Journal paper under preparation.)
- [10]Ion Stancu et al., BooNE-TN-36, Sept. 15, 2001; BooNETN-50, Feb. 18, 2002; BooNE-TN-100, Sept. 19, 2003. (Journal paper under preparation.)
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Explainable decision forest: Transforming a decision forest into an interpretable tree
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- Authors
- Roe, B. P., Yang, H.-J., Zhu, J., Liu, Y., Stancu, I., & McGregor, G.
- Affiliations
- University of Michigan (US)
- University of Alabama (US)
- Los Alamos National Laboratory (US)
- Corresponding author
- Yang, Hai-Jun
- Venue
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
- Publisher
- Elsevier BV
- Published
- 2005-05
- Volume
- 543
- Issue
- 2-3
- Pages
- 577-584
- ISSN
- 0168-9002 (ISSN-L)
- OpenAlex
- W2165960883
- Cited by
- 534
- References
- 11
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 34 · 2023: 39 · 2022: 30 · 2021: 37 · 2020: 49 · 2019: 46 · 2018: 38 · 2017: 37 · 2016: 31 · 2015: 34 · 2014: 25 · 2013: 30 · 2012: 16
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- U.S. Department of Energy
- Topics
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Neutrino Physics Research
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Particle Detector Development and Performance
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Particle physics theoretical and experimental studies
- Keywords
- particle identification, boosted decision trees, artificial neural networks, MiniBooNE, neutrino oscillations, comparison of classification algorithms, simulated data
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