Partial least squares and classification and regression trees

Abstract High-dimensional data are found in scientific fields. Difficulties arise when one applies classical classification methods to these high-dimensional data sets, because of multicollinearity. Problems with high-dimensional data sets can be overcome by reducing the dimensions of data sets. The partial least squares (PLS) method is a new method used for dimension reduction. The classification and regression trees method is applied to the reduced data for solving classification problems. A new stopping criterion for the PLS procedure is introduced. Yeh, C.H. and Spiegelman, C.H., 1994. Partial least squares and classification and regression trees. Chemometrics and Intelligent Laboratory Systems , 22: 17–23.