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Abstract: Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to Adaboost and bagging approaches. In this paper the random forest approach is extended for variable selection with other learning models, in this case partial least squares (PLS) and kernel partial least squares (K-PLS) to estimate the importance of variables. (Update)
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BibTeX entry: (Update)
@misc{ squares-random,
author = "Partial Least Squares",
title = "Random Forests Feature Selection with Kernel",
url = "citeseer.ist.psu.edu/750951.html" }
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