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数据挖掘技术在优化中药提取工艺中的应用
引用本文:朱恒民,刘文杰,王宁生.数据挖掘技术在优化中药提取工艺中的应用[J].计算机与应用化学,2006,23(3):233-236.
作者姓名:朱恒民  刘文杰  王宁生
作者单位:南京航空航天大学CIMS工程中心,江苏,南京,210016
基金项目:科技部科技攻关项目西部开发科技行动
摘    要:从中药提取工艺的历史数据中,挖掘确定提取参数的相关知识,用于指导工艺人员选择正交试验的影响因素及各因素水平。采用决策树ID3算法和支持向量分类算法,构建了提取次数的分类器;采用支持向量回归算法分别为提取时间和溶媒量建立了回归预测模型。实验结果表明,尽管ID3算法的结果可理解性较好,但支持向量分类算法有更高的精度;支持向量回归算法建立的预测模型是可靠的。

关 键 词:中药  提取  数据挖掘  决策树算法  支持向量机
文章编号:1001-4160(2006)03-233-236
收稿时间:2005-06-06
修稿时间:2005-06-062005-10-26

Application of data mining in optimization of extracting technology of traditional Chinese pharmacy
ZHU HengMin,LIU WenJie,WANG NingSheng.Application of data mining in optimization of extracting technology of traditional Chinese pharmacy[J].Computers and Applied Chemistry,2006,23(3):233-236.
Authors:ZHU HengMin  LIU WenJie  WANG NingSheng
Institution:Research Center of CIMS Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, China
Abstract:Knowledge for selecting extracting parameters was mined from past data of extracting technology of traditional Chinese pharmacy , and it could be used to direct technologist to select appropriate factors and factor levels of orthogonal test. Classifier taking extracting times as target attribute was constructed by decision tree ID3 and support vector classification algorithm. Support vector regression algorithm was applied to construct predict models for extracting time and the volume of the solvent respectively. Experiment results show that support vector classification algorithm has higher accuracy as though the output of ID3 is more comprehensive, and the predict model constructed by support vector regression is reliable.
Keywords:traditional Chinese pharmacy  extracting technology  data mining  decision tree  support vector machine
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