Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Han Long, Mark J. Embrechts, Boleslaw Karol Szymanski, Karsten Sternickel, Alexander Ross · 2006
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 forests 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. This variable selection method is demonstrated on two benchmark datasets (Boston Housing and South African heart disease data). Finally, this methodology is applied to magnetocardiogram data for the detection of ischemic heart disease.