Spectral Analysis for Origin Identification of Chinese Herbal Medicines Based on Stacking Classification Models
Pages 586 - 590
Abstract
This paper demonstrates the operability and effectiveness of a Stacking ensembled classification model applied to infrared spectral analysis for identifying the origin of Chinese herbal medicines. The study focuses on dimensionality reduction based on feature importance weights through the embedding method, which proves to be the most stable and effective approach in this context. The results show that the ensembled Stacking classification model outperforms single classification models, leading to accurate and effective predictions in the test dataset.
References
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Heidrich Daiane Koehler Alessandra, Ramírez- Castrillón Mauricio, Pagani Danielle Machado, Ferrão Marco Flores, Scroferneker Maria Lúcia and Corbellini Valeriano Antonio. 2021. Rapid classification of chromoblastomycosis agents genera by infrared spectroscopy and chemometrics supervised by sequencing of rDNA regions. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Volume 254(2), 119647 pages. https://doi.org/10.1016/j.saa.2021.119647.
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XIE Xiao-min. 2022. Identification of Species and Origin of Chinese Herbs by Infrared Spectroscopy Analysis. Vol. 36 No. 4. Journal of NANTONG vocational university, 57 pages – 63 pages, https://doi.org/10.3969/j.issn.1008-5327.
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Index Terms
- Spectral Analysis for Origin Identification of Chinese Herbal Medicines Based on Stacking Classification Models
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Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 17 April 2024
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EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
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Overall Acceptance Rate 508 of 972 submissions, 52%
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