Random Forests Feature Selection with Kernel Partial Least Squares: Detecting Ischemia from MagnetoCardiograms

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. 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.