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Original Articles Open Access
The 2 T ellipse assisted analysis method of the partial lea st squares (PLS) is able to recognize the noise, ho wever, it fails to analyze the noise in the multidimensional space. In the paper, we propose the slacks-based me asure ( SBM ) algorithm to optimize PLS. Firstly, evaluating the sample data comprehensively with SBM, we can gain t he valid data. Secondly, analyzing the data based on the PLSR. The two steps can avoid the impact which the noise dat a have on the regression accuracy and make up the aided analysis technology of the PLSR. Through the calculation of traditional Chinese medicine (TCM) experiments, for two depende nt variables, we find out that the average relative errors of the optimized PLS with SBM algorithm are 5.0844% and 8. 7485%, which are lower than the results ( 5.5825% a nd 9.2810% ) by using the PLSR. Besides, for a single dependent variable of the data of tool wear test, t he average relative error optimized by SBM is 2.6984%, which i s lower than 3.3526% calculated by utilizing the PL S. The experiments result indicate that the regression pre cision of the PLSR optimized by SBM is much higher than PLSR.
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Author(s): Jianqiang Du Zhulin Hao Guolong Wang Riyue Yu Bin Nie and Wangping Xiong
DEA, slacks-based measure, PLS, auxiliary analysis technology, partial least squares algorithm, SBM-DEA