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Compared with the Naïve Bayes and KNN algorithms, SVM has the advantages of high classification speed, high accuracy of the training data size, and universal property.
That is, the base learners utilized in a robust ensemble classifier should be of high classification accuracy and avoid making coincident misclassification errors which in turn necessitate the diverse learners.
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Three top-down and one bottom-up approaches are evaluated for retrieving the most appropriate feature sets in terms of highest classification rates.
A multitude of experiments demonstrate three aspects: a) results of attribute reduction using GCRS model are proved to be of higher classification accuracies; b) attribute reduction based on GCRS model for big data manifests faster speed; c) results of rough self-learning experiments illustrate the validity of the proposed theory.
The logistic regression model was slightly less successful than GENN (77.3% overall correct), and followed the same trend of higher classification rates for cases than controls.
In pursuit of higher classification accuracy, the feature selection method we used here is a wrapper method.
As it can be noted from Table 3, in 5 cases out of 8, ICA-based RotBoost learners could outperform their PCA-based counterparts in terms of higher classifications accuracies and lower standard deviations.
Further, random forest classification [ 60] is a relatively novel method of determining variable importance, with the advantages of very high classification accuracy and no restrictions regarding the distribution and scaling properties of the data [ 79].
This satisfied the requirements of the high classification accuracy and the low delay time for the prosthetic control system based on the SEMG signals.
The machine learning algorithm selects the optimal set of attributes with high classification accuracy.
Generally, most related algorithms aim to identify the minimum number of selected genes with high classification accuracy.
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