Signal Classification Using Random Forest with Kernels
Jiguo Cao, Guangzhe Fan · 2010
Signal classification is an area of much interests in signal processing. Traditional classification methods designed for discrete variables are limited in its power. 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 (KIRF) for signal data. It is novel with respect to KIRF because a new type of kernel suitable for signal data is proposed and applied. We use two examples, a phoneme 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. Evidences from the data show that our results are significantly better than those traditional methods for signal classification.