摘要
应用于中医脉象信号分类研究中的多种方法提取了大量复杂特征,但使用时由于缺乏系统分析而难以在算法中高效利用,本文提出了一种基于随机森林的脉象信号特征评估降维方法。首先,提取常用的脉象时域、频域以及时-频域特征共93维;随后,使用随机森林算法,基于Gini指数对各个特征重要性进行排序,并使用支持向量机(SVM)、反向传播神经网络(BP-NN)以及随机森林(RF)算法验证排序的正确性,最后,结合序列前向选择算法,根据算法的分类准确率变化进行特征选择。实验结果表明:基于随机森林算法的脉象特征重要性排序可行,且进行特征筛选后,特征维数从93维降低到13维左右,对平、实、弦、滑四类脉象的分类,SVM和BP-NN的准确率均提高了10%以上,对特征冗余性不敏感的RF算法分类准确率也提高了4.5%,该方法可用于脉象信号分析中大量特征的评估降维,可显著提高算法的分类准确率和运行效率。
The various methods for Traditional Chinese Medicine(TCM)pulse signal classification have extracted a large number of complex features,but it is difficult to use them efficiently in the classification algorithms due to the lack of systematic analysis on these features.This paper proposed a method for features evaluation and dimension reduction of pulse signal based on random forest.Firstly,the time domain,frequency domain and time-frequency domain features of pulse signal were extracted in 93 dimensions.Subsequently,the random forest algorithm was used to sort the importance of each feature based on the Gini index.Support vector machine(SVM),back propagation neural network(BP-NN)and random forest(RF)algorithm were used to verify the correctness of the ranking.Finally,combined with the sequence forward selection algorithm,the feature selection was performed according to the classification accuracy of each algorithm.The experiments results showed that the ranking of the importance of these features based on the random forest algorithm was feasible,and after the feature selecting,the feature dimension decreased from 93 to 13.For the classification of normal,shi,wiry and slippery pulse,the accuracy of SVM and BP-NN increased by more than 10%,and the classification accuracy of RF which is insensitive to feature redundancy also increased by 4.5%.As a result,this method can be used to a large number of features evaluation and dimension reduction in wrist pulse signal analysis,and improve the classification accuracy of the algorithm with efficiency.
作者
张诗雨
杨珂
夏春明
金陈玲
王忆勤
燕海霞
Zhang Shiyu;Yang ke;Xia Chunming;Jin Chenling;Yan Haixia;Wang Yiqin(School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai,200237,China;Laboratory of Information Access and Synthesis of Traditional Chinese Medicine Four Diagnosis,Shanghai University of Traditional Chinese Medicine,Shanghai,201203,China)
出处
《世界科学技术-中医药现代化》
CSCD
北大核心
2020年第7期2418-2426,共9页
Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology
基金
国家自然科学基金委员会面上项目(81673880):基于中医四诊大数据的冠心病风险评估与预测模型研究,负责人:王忆勤
关键词
脉象分类
特征提取
特征降维
随机森林
Pulse signal classification
Feature extraction
Feature reduction
Random forest
