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The selection of classifications of nutritional status in children and adolescents, references, cut-offs, and terms to be used in different contexts are issues with moral foundations and implications, e.g., who should be treated and how we assess success.
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The selection of classification variables was supported by a principal component analysis.
Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method.
In the remainder of this section, we present the used classification algorithms and tools, the training set construction, the generation of input data for classification, and finally, the evaluation and selection of classification algorithms.
To construct a well-suited electroencephalogram (EEG) decoding module for BHCR, we conducted a comparative inspection on the selection of classification algorithms, and adopted linear discriminant analysis and random forests as a feature extraction method and classification algorithm, respectively.
Parameters can be filtered by a selection of classification tags, such as parameters associated with a particular disease.
One of the aspects of signal classification is the selection of proper classification features.
Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.
The selection of any classification technique depends on the type and nature of data.
The selection of particular classification algorithms was dictated by their popularity in virtual screening experiments.
Selection of this classification scheme is based on literature reviews and author's experience of study area and its condition (Al-Abadi 2015a).
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