|
|
|
|
|
|
| Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling |
| CHEN Chu-han1, ZHONG Yang-sheng2, WANG Xian-yan3, ZHAO Yi-kun1, DAI Fen1* |
1. College of Electronic Engineering，South China Agricultural University，Guangzhou 510642, China
2. College of Animal Science，South China Agricultural University，Guangzhou 510642, China
3. Guangdong Sericulture Technology Promotion Center，Guangzhou 510640, China
|
|
|
|
|
Abstract The cost of identifying male and female cocoons by NIR is high，and the cost can be reduced by selecting useful features. Since there is a nonlinear relationship between the NIR spectra of female and male cocoons, a wrapper feature selection method, Bootstrapping Re-weighted Sampling Support Vector Machines (BRS-SVM), was proposed. The diffuse transmission NIR spectra of silkworm cocoons were collected by NirQuest512 NIR spectrometer. The heat map of characteristic importance was obtained by modeling the whole band of the test set, and the heat map obtained the range of important characteristic bands. Then, in the range of important characteristic bands, the single band features and continuous band area features were selected by BRS-SVM, Model-based ranking support vector machines (MBR-SVM), Model-based ranking Logistic Regression feature sorting method (MBR-LR), Recursive feature elimination (RFE), successive projections algorithm(SPA), Genetic Algorithm(GA), and then the support vector machines (SVM) and Logistic Regression (LR) sex classification models were established respectively. According to the characteristic importance heat map, it is found that the important area of male and female classification of silkworm cocoon was within 900~1 399 nm. We used this band to build the SVM model, and achieved 99.40% accuracy. BRS-SVM was used to select 5 single-band features. The accuracy of the test set is 89.56%, which is 2%~4% higher than other feature selection methods. RS-SVM was used to select 27 single-band features, and the accuracy of the test set of the SVM gender classification model was 94.97%, which reached the requirements of production conditions. The accuracy of modeling test set by BRS-SVM was 94.43% for 14 continuous band features. In the case of selecting a small number of features, our proposed BRS-SVM is superior to other methods. Using BRS-SVM to select a small number of features, we can establish a good performance of the female and male cocoon classification model, effectively reduce the cost, has important practical significance.
|
|
Received: 2021-07-13
Accepted: 2021-11-03
|
|
|
|
Corresponding Authors:
DAI Fen
E-mail: sunflower@scau.edu.cn
|
|
[1] FENG Wei-song(封槐松). China Sericultur(中国蚕业)，2018，(1): 1.
[2] ZHAO Ming-yan，JIANG Xin-yu，NIU Bao-long，et al(赵明岩，蒋昕余，牛宝龙，等). Science of Sericulture(蚕业科学)，2018，44(5): 711.
[3] Tao D，Wang Z，Li G，et al. Spectroscopy Letters，2018，51(8): 446.
[4] YAN Hui，LIANG Meng-xing，GUO Cheng，et al(颜 辉，梁梦醒，郭 成，等). Science of Sericulture(蚕业科学)，2018，44(2): 283.
[5] Zhu Z，Yuan H，Song C，et al. Sensors and Actuators B: Chemical，2018，268: 299.
[6] DAI Fen，CHE Xin-xin，PENG Si-ran，et al(代 芬，车欣欣，彭斯冉，等). Journal of South China Agricultural University(华南农业大学学报)，2018，39(2): 103.
[7] HONG Bin，DENG Bo，PENG Fu-yang，et al(洪 斌，邓 波，彭甫阳，等). Chinese Journal of Computer(计算机科学)，2016，43(8): 19.
[8] LI Yu-qiang，PAN Tian-hong，LI Hao-ran，et al(李鱼强，潘天红，李浩然，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2019，39(12): 3809.
[9] JIAO Lei-zi，DONG Da-ming，ZHAO Xian-de，et al(矫雷子，董大明，赵贤德，等). Smart Agriculture(智慧农业)，2020，2(2): 59.
[10] WANG Ling，LI Ding-ming，QIAN Hong-juan，et al(王 玲，李定明，钱红娟，等). Chinese Journal of Analysis Laboratory(分析试验室)，2016，35(10): 1203.
[11] Shardlow M. An Analysis of Feature Selection Selection Techniques, Mathematics, Computer Sciences, 2011.
[12] Zhang J，Xiong Y，Min S. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2019，223: 117110.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [15] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
|
|
|
|
