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The paper also discusses the effect of features, effect of various classifier parameters on classification accuracy.
Table 4 shows the comparison between various classifier IDMs.
That most combination rules gave the same results can be explained by the fact that these simple rules were actually developed from "sum" and "product" rules as detailed in ([53], Chapter 5). Figure 6 Performance comparison of classifier fusion of EEG and HRV using various classifier combination rules.
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Table 1 Groupwise average accuracies of various feature extraction methods combined with various classifiers applied to classify sets A, D, and E (standard deviations are noted in parentheses).
Table 2 Groupwise average accuracies of various feature extraction methods combined with various classifiers applied to classify sets A, B, C, D, and E (standard deviations are noted in parentheses).
The advantages and limitations pertaining to the development of the various classifiers were discussed.
Performance of the various classifiers was compared by the error rate.
A set of rules has been generated for making final classification decision based on outputs from various classifiers.
Experimental results on several databases suggest that using the active vision processing can improve classification rates implemented with various classifiers.
The first method consists of using the outputs of various classifiers as inputs to a second neural net fuser.
For this purpose, a rigorous set of experiments are performed using various classifiers such as SVM, Decision Tree, W-J48 and KNN.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com