首先利用RF-RFECV方法对混合运行数据进行K折交叉验证学习与重要性排序,抽取并重构故障特征信息;将预处理后的数据作为输入样本,利用PSO与序列最小优化算法(SMO)搜索超参数得到最佳SVM分类器,实现故障诊断.应用于田纳西-伊斯曼(Tennessee Eastman, TE)过程的仿真实验结果表明:RF-RFECV与PSO-SVM融合故障诊断方法泛化能力强,诊断准确率高,识别准确率可达到99.5%以上.doi:10.16351...
Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI dataConsidering the unsatisfactory classification accuracy of autism due to unsuitable features selected in current studies, a functional connectivity (FC)-based algorithm for classifying autism and control using ...
A fault diagnosis method of heating, ventilation, and air conditioning (HVAC) systems based on the ReliefF-recursive feature elimination based on cross validation-support vector machine (ReliefF-RFECV-SVM) combined model is proposed to enhance the diagnosis accuracy and efficiency. The method ...
W-SVM特征选择算法优化为了实现对齿轮故障类型的准确诊断,提出了一种基于RFECV-RF特征选择的W-SVM故障诊断分类算法.为避免特征冗余带来的偏差,采用RFECV-RF算法对特征变量进行重要度评估,与多个特征选择方法进行了对比.基于数据所具有的不均衡性,对SVM分类开展算法优化,引入了加权支持向量机(W-SVM),使用网格搜索进行超...
Then the support vector machine recursive feature elimination cross-validation (SVM-RFECV) is used to select among the preselected feature wavebands to obtain the final modeled wavebands, and the Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Partial...