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These irrelevant features usually have a disrupting effect on the classification accuracy.
They overlap with the discriminative modulations, which present a negative effect on the classification.
It is observed that change of noise model or frequency offset has almost no significant effect on the classification rate.
Thus the data imbalance will cause the insufficient study on properties of default sample, which has bad effect on the classification accuracy of both SVM and BP model.
However, in a multi-subject level, the temporal compression improved the performance of the SVM, but the space selection had no effect on the classification accuracy.
This is crucial in thin cementitious laminates since damping, scattering, reflections and plate wave dispersion seriously distort the signal having a strong effect on the classification result.
Similar(48)
To determine the effect of the classification accuracy on the number of nearest neighbors (k), the classifications were performed with k varying from three to eight.
Nonetheless, the structure of the decision tree reveals that some input features have no effects on the classification performance.
Limiting the number of proteins in the profile generally had minor effects on the classification accuracy.
Additional features may have detrimental effects on the classification such as slowing down the learning process and causing overfitting the training data.
Limiting the number of proteins in the profiles (ranging from 568 to 10) had only minor effects on the classification performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com