Abstract
The purpose of classification for metabolomics data is finding a subset of metabolites called marker candidate which can separate groups efficiently as well as discriminating the groups. We evaluate and compare 5 classification methods on 26 real datasets, and provide the guidelines for finding marker candidate from appropriate classification method. Although this study shows that the predictive accuracies from 5 methods are sufficiently higher (more than 90%) in 19 cases among 26 datasets, PLSDA and SDA give better performance than other methods from the aspects of classification accuracy and metabolites selection.
