Explanatory System of Support Vector Regression and Its Application in QSPR of Surfactants  

具解释能力的支持向量回归应用于表面活性剂QSPR研究（英文）

作　　者：谭显胜[1,2] 金晨钟[1] 李巍巍[1] 袁哲明[2] 

机构地区：[1]湖南人文科技学院农业与生物技术学院,湖南娄底417000 [2]湖南农业大学植物保护学院,湖南长沙4101281

出　　处：《Agricultural Science & Technology》2016年第11期2452-2456,共5页农业科学与技术（英文版）

基　　金：Supported by Industrialization Cultivation Projects in Colleges and Universities of Hunan Province(13CY030);Natural Science Foundation of Hunan Province(12JJ6026);Colleges and Universities Open Innovation Platform Fund of Hunan Province(14K053,15K066)~~

摘　　要：In order to solve the problem of poor interpretability of support vector re- gression （SVR） applied in quantitative structure-property relationship （QSPR）, a com- plete set of explanatory system for SVR was established based on F-test, The nov- el explanatory system includes significance tests of model and single-descriptor im- portance, single-descriptor effect and sensitivity analysis, and significance tests of interaction between two descriptors, etc. The results of example indicated that the explanatory results of the new system were consistent well with those of stepwise linear regression model and quadratic polynomial stepwise regression model. The explanatory SVR model will play an important role in regression analysis such as QSPR.基于结构风险最小的支持向量机具泛化推广能力优异等诸多优点,在分类和预测领域应用广泛,但其可解释性差的缺陷一直未获根本性解决。基于F测验为支持向量回归建立了一套完整的解释性体系,包括模型显著性测验、单描述符重要性显著性测验、单描述符效应及灵敏度分析、两描述符互作显著性测验等。将其应用于农药中常用阴离子表面活性剂的定量构质关系研究,实例验证表明,其解释结果与逐步线性回归模型及二次多项式逐步回归模型结果基本一致,且支持向量回归模型性能明显优于参比模型,表明了该解释性体系的合理性。

关 键 词：Support vector regression Explanatory system SURFACTANT Significant test Quantitative structure-property relationship 

分 类 号：TQ423.11[化学工程] TQ450.4