PLS-DA is a linear classification method, which cannot fully express the nonlinear relationship between data, and is difficult to adapt to the classification and identification of nonlinear data. Aiming at this problem, with softmax regression can express the nonlinear relationship, the paper proposed a partial least squares discriminant analysis algorithm fusing softmax(PLS-S-DA). In order to verify the validity of PLS-S-DA for nonlinear data, it used the accuracy, run time, precision, recall and F₁-score as evaluation indicators. It used four UCI data sets and Chinese herbal medicine data set for testing. The performance of PLS-S-DA was compared with five classification algorithms such as PLS-DA. The results show that for data with nonlinear characteristics, PLS-S-DA has higher accuracy than other algorithms, and has stronger recognition ability for cold medicine and hot medicine.

[2019-12-05] Printed Article /doc/2018.08.0592/v2