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Guang-Hui Fu, Dong-Sheng Cao, Qing-Song Xu, Hong-Dong Li and Yi-Zeng Liang Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors Journal of Chemometrics 25

Version of Record online: 1 FEB 2011 | DOI: 10.1002/cem.1364

In this paper, a two-step nonlinear classification algorithm is proposed to model the structure-activity relationship (SAR) between bioactivities and molecular descriptors of compounds, which consists of kernel principal component analysis (KPCA) and linear support vector machines (KPCA+ LSVM). The combination of KPCA and LSVM can effectively improve the prediction performance compared with the linear SVM as well as two nonlinear methods. Three datasets related to different categorical bioactivities of compounds are used to evaluate the performance of KPCA+LSVM. The results show that our algorithm is competitive.

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