Analysis of TCM data based on partial least square and model tree
DU Jian-qiang
YU Fang
NIE Bin
ZHU Zhi-peng
LIU Lei
Abstract：Traditional Chinese medicines (TCM) present features of more compositions,more targets and more efficacies.Therefore,the collected data of TCM exist multi-components,multi-targets and nonlinear characteristics.Partial least square (PLS) can't adapt to the characteristics of the TCM data due to its own nonlinear regression.However,model tree (MT),which is made up of many multiple linear segments,has a good fitting effect to nonlinear data.Based on this,a new method combining PLS and MT to analysis and predict the data is proposed,employ native PLS method to extract main ingredients continually and accumulate it,then build Model Tree through the main ingredients and the original explanatory variables one step by step,until the precision requirements are met.Using the data of the maxingshigan decoction of the monarch drug to treat the asthma or cough and five sample sets in the UCI machine learning repository,the experimental results showed that the PLS and model tree have good adaptability for the TCM data.
Keywords：partial least squaresnonlinearTCM informationmodel tree
Publication Date：2017-01-01
Online Publishing Date：2026-08-14(First online date of this platform, not the publication date of the document)
Pages：6( 1997-2002 )