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Genetic algorithm is used to get the best classification accuracy of AdaBoostSVM.
The best classification accuracy of MLPNN was obtained as 90% (specificity: 100% and sensitivity: 83.3%) for participant 7. On the other part, the best classification accuracy of kNN was obtained as 85% for participant 12 and 20.
In OV, the best classification accuracy of long versus short survival was 83.33%, using four features.
For GBM, the best classification accuracy of long versus short survival was 82.61%, using 20 selected features.
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It provides the best classification accuracy (95.3% of accuracy) in comparison with others.
In order to acquire the best classification accuracy, appropriate value of k was determined as different for each participant.
While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy.
It was shown that the SCSP algorithm achieved the best classification accuracy by reducing the number of channels and an improvement of 10%% in classification accuracy compared to the three channels case (C3, C4, and Cz).
This is not the case for the Unique dataset, where the SVMs achieve the best classification accuracy when employing the fusion of the cortical features, the MFCCs, and the chroma features.
Our results have indicated that the combination of the two learning algorithms provides the best classification accuracy.
Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).
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CEO of Professional Science Editing for Scientists @ prosciediting.com