Feature subset selection method for AdaBoost training

The feature-selection problem in training AdaBoost classifiers is addressed in this paper.A working feature subset is generated by adopting a novel feature subset selection method based on the partial least square(PLS)regression,and then trained and selected from this feature subset in Boosting.The experiments show that the proposed PLS-based feature-selection method outperforms the current feature ranking method and the random sampling method.