Sp ectral Feature Selection for Automated Rock Recognition using Gaussian Process Classiflcation
Hongping Zhou, Sildomar T. Monteiro, Peter J. Hatherly, Fabio Tozeto Ramos, Eric W. Nettleton, Florian Oppolzer · 2009
A spectral feature selection scheme is proposed for multi-class automated rock recognition from real world drilling data using Gaussian Process classiflcation. This work is part of a larger project aimed at surface mine automation. The motivation for this research is to investigate which combination of drilling data measurements is most relevant for rock recognition. We conduct feature selection in the frequency domain where characteristics are more distinguishable. In particular, we extended the spectral feature selection method from binary classiflcation to multi-class classiflcation by decomposing the multi-class classiflcation dataset into a series of one versus one binary classiflcation datasets. A non-uniform discrete Fourier transform (NDFT) is then applied to data on each of the binary classiflcation features, where the features with the most consistent \major bandwidth across all decomposed binary classiflcations are selected. The approach has been applied on multi-class rock recognition (based on drilling data) and results are presented on real world drilling data.