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The selection of classification variables was supported by a principal component analysis.
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.
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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.
One of the aspects of signal classification is the selection of proper classification features.
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.
However, the selection of PSI classification thresholds is non-trivial.
One is feature extraction and selection and the other is the selection of the classification algorithm.
Given that the selection of a classification leads to important differences in the clinical conditions which are underscored; these choices should be made explicit and justified with respect to the objective of the analyses.
This chapter presents a systematic account of the conventional and modern approaches regarding the selection of ingredients, their classification, role, incorporation techniques, interaction with a variety of matrices and performance evaluation.
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.
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