Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data
Date
2016-11
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Ensemble classifiers are being widely used for the classification of spectroscopic data. In this
regard, the random forest (RF) ensemble has been successfully applied in an array of applications,
and has proven to be robust in handling high dimensional data. More recently, several variants
of the traditional RF algorithm including rotation forest (rotF) and oblique random forest (oRF)
have been applied to classifying high dimensional data. In this study we compare the traditional
RF, rotF, and oRF (using three different splitting rules, i.e., ridge regression, partial least squares,
and support vector machine) for the classification of healthy and infected Pinus radiata seedlings
using high dimensional spectroscopic data. We further test the robustness of these five ensemble
classifiers to reduced spectral resolution by spectral resampling (binning) of the original spectral
bands. The results showed that the three oblique random forest ensembles outperformed both the
traditional RF and rotF ensembles. Additionally, the rotF ensemble proved to be the least robust
of the five ensembles tested. Spectral resampling of the original bands provided mixed results.
Nevertheless, the results demonstrate that using spectral resampled bands is a promising approach
to classifying asymptomatic stress in Pinus radiata seedlings.
Description
CITATION: Poona, N., Van Niekerk, A. & Ismail, R. 2016. Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data. Sensors, 16(11):1918, doi:10.3390/s16111918.
The original publication is available at www.mdpi.com
Publication of this article was funded by the Stellenbosch University Open Access Fund.
The original publication is available at www.mdpi.com
Publication of this article was funded by the Stellenbosch University Open Access Fund.
Keywords
Spectroscopy -- Imaging, Oblique tree-based ensembles, Pinus radiata, Sensors
Citation
Poona, N., Van Niekerk, A. & Ismail, R. 2016. Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data. Sensors, 16(11):1918, doi:10.3390/s16111918