Signal Classification Using Random Forest with Kernels

Abstract

Here we propose a novel approach for some signal classification problems. It is a combination of three artificial intelligence approaches: tree-based approach, ensemble voting and kernel learning. We call this approach kernel-induced random forest. We use two examples, a phenome speech data and a waveform simulation data to illustrate its usage and evidences of improving on traditional methods such as neural networks and discriminant methods.