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.
Key words：Cocoons; Near infrared spectrum; Feature selection
陈楚汉，钟杨生，王先燕，赵懿琨，代 芬. SVM自助重加权采样的蚕茧雌雄特征波长选择[J]. 光谱学与光谱分析, 2022, 42(04): 1173-1178.
CHEN Chu-han, ZHONG Yang-sheng, WANG Xian-yan, ZHAO Yi-kun, DAI Fen. Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1173-1178.
