The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
pandangan teks lengkap
115
Sindrom adalah prinsip penting Perubatan Tradisional Cina. Klasifikasi formula ialah pendekatan yang berkesan untuk menemui gabungan herba untuk rawatan klinikal sindrom. Dalam kajian ini, algoritma kelip-kelip berasaskan carian tempatan (LSFA) untuk pengoptimuman parameter dan pemilihan ciri mesin vektor sokongan (SVM) untuk klasifikasi formula dicadangkan. Parameter C dan γ SVM dioptimumkan oleh LSFA. Sementara itu, keberkesanan herba dalam klasifikasi formula diguna pakai sebagai ciri. LSFA mencari subset ciri yang berprestasi baik untuk memaksimumkan ketepatan pengelasan. Di LSFA, pencarian kelip-kelip tempatan dibangunkan untuk menambah baik FA. Simulasi menunjukkan bahawa algoritma LSFA-SVM yang dicadangkan mengatasi algoritma pengelasan lain pada set data yang berbeza. Parameter C dan γ dan ciri-ciri dioptimumkan oleh LSFA untuk mendapatkan prestasi pengelasan yang lebih baik. Prestasi FA dipertingkatkan oleh mekanisme carian tempatan yang dicadangkan.
Wen SHI
the Tianjin University of Commerce
Jianling LIU
the Tianjin University of Commerce
Jingyu ZHANG
the Tianjin Nankai Hospital
Yuran MEN
the Tianjin University of Commerce
Hongwei CHEN
the Tianjin University of Commerce
Deke WANG
the Tianjin University of Commerce
Yang CAO
the Tianjin University of Commerce
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Salinan
Wen SHI, Jianling LIU, Jingyu ZHANG, Yuran MEN, Hongwei CHEN, Deke WANG, Yang CAO, "Feature Selection and Parameter Optimization of Support Vector Machines Based on a Local Search Based Firefly Algorithm for Classification of Formulas in Traditional Chinese Medicine" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 5, pp. 882-886, May 2022, doi: 10.1587/transfun.2021EAL2075.
Abstract: Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAL2075/_p
Salinan
@ARTICLE{e105-a_5_882,
author={Wen SHI, Jianling LIU, Jingyu ZHANG, Yuran MEN, Hongwei CHEN, Deke WANG, Yang CAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Feature Selection and Parameter Optimization of Support Vector Machines Based on a Local Search Based Firefly Algorithm for Classification of Formulas in Traditional Chinese Medicine},
year={2022},
volume={E105-A},
number={5},
pages={882-886},
abstract={Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.},
keywords={},
doi={10.1587/transfun.2021EAL2075},
ISSN={1745-1337},
month={May},}
Salinan
TY - JOUR
TI - Feature Selection and Parameter Optimization of Support Vector Machines Based on a Local Search Based Firefly Algorithm for Classification of Formulas in Traditional Chinese Medicine
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 882
EP - 886
AU - Wen SHI
AU - Jianling LIU
AU - Jingyu ZHANG
AU - Yuran MEN
AU - Hongwei CHEN
AU - Deke WANG
AU - Yang CAO
PY - 2022
DO - 10.1587/transfun.2021EAL2075
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2022
AB - Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.
ER -