Random Forests Feature Selection with K-PLS: 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 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. 1 Partial Least Squares (PLS) and K-PLS Partial Least Squares Regression (PLS) was introduced by Herman Wold [1] for econometrics modeling of multi-variate time series. PLS can be viewed as a "better" Principal Components Analysis (PCA) regression method, where the data are first projected into a different and non-orthogonal...

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Long Han, Mark J. Embrechts, Boleslaw K. Szymanski, Karsten Sternickel, Alexander Ross
• Keywords: ESANN 2006, ESANN 2007, Partial Least Squares, Variable Selection, Variable Selection Methods