基于改进RF特征选择策略的烤烟油分高光谱特征分析

针对烤烟油分特征预测模型的特征优选问题,提出一种改进RF(随机森林)算法特征选择策略,首先通过RF特征选择算法计算出各个特征的RF-Score,将特征按RF-Score的大小排序依次添加到特征子集中,若分类器分类准确率提高则保留该特征,若分类器分类准确率没有提高或降低则去除该特征.结果 表明:利用RF特征选择算法对烤烟...

Analysis of hyperspectral characteristics of flue cured tobacco oil based on improved RF feature selection strategy

Aiming at the feature selection problem of the flue cured tobacco oil feature prediction model, an improved RF (random forest) algorithm feature selection strategy was proposed. First, the RF Score of each feature was calculated by the RF feature selection algorithm, and the features were added to the feature subset in order according to the size of the RF Score. If the classification accuracy of the classifier was improved, the feature was retained. If the classification accuracy of the classifier was not Increase or decrease, the feature was removed. The results show that when the RF feature selection algorithm features of the hyperspectral was used to screen flue cured tobacco, 176 high spectral characteristics in descending order the Gini coefficient was input in turn to the SVM classifier. The first 64 hyperspectral band features can make the support vector machine classifier perform the best. The dimension of the feature subset was 64, and the classification accuracy was 93.33%. Using the improved RF feature selection strategy of 176 high flue cured tobacco characteristics of spectral band selection, entering only six band hyperspectral characteristics, 371.08 nm, 716.71 nm, 378.31 nm, 487.77 nm, 484.09 nm, and 535.85 nm, will optimize the performance of the support vector machine classifier. The classification accuracy was 95%, and the dimension of the feature subset was 6, suggesting that the improved RF feature selection strategy can reduce the dimensionality of the data and reduce the feature set while ensuring the performance of the classifier. Compared with the full hyperspectral band, the number of features of the improved RF feature selection algorithm was reduced was 170 and the classification accuracy was improved by 3.33%. Compared with the RF feature selection algorithm, the number of features was reduced by 58, and the classification accuracy was increased by 1.67%.