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Obviously, texture (i.e. scattering) characteristics further contribute in the classification of plaque components with similar attenuation properties (i.e. fibrotic and calcified tissues).
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We implement a Naive Bayes (NB) classifier (sometimes harshly called idiot Bayes [43]) on the tagged movie review dataset to examine how individual words contribute in machine learning classification.
Biologically, it is of interest to identify the features that significantly contribute to the classification in that it will reveal the relationships between input features and outcome.
In particular, it should contribute to the classification of the abundant unknown viruses found on assembled contigs of metagenomic samples.
Feature selection results in a ranking of 35 predictors that contribute to the classification of NT1 and NT2 (see Supplementary Figure 1).
Figure 2b displays the classifier voxel weights that reliably contribute to the classification of pain and rejection, respectively, based on bootstrap tests with 10,000 iterations (thresholded at P<0.001 uncorrected for display only; all voxel weights were used in classification).
We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity.
This results in having, for any rule generated, an identical rule with B appended, that does not contribute to the classification and lengthens the model.
However, not all features contribute to the classification.
In most applications of arrayCGH classification, it can be expected that only a limited number of regions on the genome should contribute to the classification, because most parts of the genome may not differ between populations.
However, all sources of information (i.e. optical, radar and topography) featured prominently in contributing to the classification tree procedure, reinforcing the idea that multi-source data are useful in the characterization of wetland land cover.
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