The Classification Tree Combined with SIR and Its Applications to Classification of Mass Spectra

by Ping He, Kai-Tai Fang and Cheng-Jian Xu

Journal of Data Science, v.1, no.4, 425-445

Abstract

A new approach combining classification tree (CT) with sliced inverse regression (SIR) is proposed and applied to the classification of mass spectra in this paper. The classification tree has been widely used to generate classifiers from the mass spectral data because of its powerful ability in automatic variable selection and automatic interaction detection. However, it is often weak on presenting the linear and global relationships among variables. When the variables enter a model with the form of linear combination, the classification tree can not detect the form and leads to a low accuracy. SIR is an effective method to find useful linear combinations of predictor variables to regress the response variable. So merging CT and SIR harmoniously can inherit both advantages of them. Experiments in the paper show that the proposed approach can improve classification accuracy of decision tree and get better result than other classical classification methods.

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