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The Least Squares-Support Vector Machine (LS-SVM) with Morlet wavelet and Radial Basis Function (RBF) kernels obtained the highest classification accuracy of 100% for the diagnosis of CAD.
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The g i (g i ∉ G N) associated with the group, G N ∪{ g i} that obtains the highest classification accuracy, is the candidate for g N +1 (not yet g N +1).
Random selection maintained an average classification accuracy rate above 85%% for 10 50 electrodes and obtained the highest average classification accuracy rate of 89.6 % with 32 electrodes.
It obtained the highest average classification accuracy rate of 91.7 % with 22 electrodes.
Similarly, [34] concluded that they obtained the highest accuracy for their classification problem using a CFS as compared to other feature selection methods.
Based on the methodology used in this systematic review, Anderson's[7] and Althabe's[15] classifications obtained the highest grades and the best performance for indication-based classifications.
Robson's 10-group model[6] was in first place among the women-based classifications and obtained the highest overall grade and best performance on the case-scenarios.
The lowest classification result was obtained with a short hair feature, reaching 53 % accuracy, and the highest classification result was obtained with a "carrying" feature, reaching 75%% accuracy.
The highest classification accuracy obtained is 96.55% with sensitivity, specificity of 0.966 and 0.987 respectively.
For the meat samples stored at 10 °C, the highest classification accuracies obtained by LDA method with leave-1-out and bootstrapping validations were 83.8% and 89.1%, respectively.
The highest classification accuracies obtained for the samples stored at 4 °C were 85% and 96% by using QDA method with leave-1-out and bootstrapping validations, respectively.
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CEO of Professional Science Editing for Scientists @ prosciediting.com