Feature subset selection method for AdaBoost training
Journal of Beijing Institute of Technology (English Edition)(2011)
Abstract
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.
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Key words
Boosting method,Dimensionality reduction,Feature subset
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