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Tree species composition is an indicator of forest type. It is also a required attribute in forest inventory, biomass and stand volume estimation. Accurate mapping tree species is essential for forest management purposes. In this paper the performances of LiDAR, RapidEye data, and their combination on tree species classification were investigated in a boreal forest. Both Random forest (RF) and support...
Accurate estimation of the canopy chlorophyll content of a crop is essential for crop production. Ground-based hyperspectral datasets were obtained under a wide range of plant and environmental conditions in Jilin using Analytical Spectral Devices(ASD) spectroradiometers, and canopy chlorophyll content in canopy were measured by Soil and Plant Analyzer Development(SPAD)-502. The objective of this...
Synthetic Minority Oversampling TEchnique (SMOTE) is a popular oversampling method that was proposed to improve random oversampling but its behavior on high-dimensional data has not been thoroughly investigated. In this paper we evaluate the performance of SMOTE on high-dimensional data, using gene expression microarray data. We observe that SMOTE does not attenuate the bias towards the classification...
The goal of class prediction studies is to develop rules to accurately predict the class membership of new subjects. The classifiers differ in the way they combine the values of the variables available for each subject. Frequently the classifiers are developed using class-imbalanced data, where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced...
Metabonomics is an emerging field providing insight into physiological processes and difference. Besides conventional PCA, PLS and OPLS approaches, more and more machine learning classifiers are likely to become the supplements for metabolic profiling data analysis. A comprehensive comparison of PLS, support vector machine (SVM, with linear and quadratic kernels), linear discriminant analysis (LDA),...
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