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In the last part of the study, we evaluated feature-level fusion by using three classifiers.
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This is performed by using two classifiers, a first one for detecting motion-speed-related events and a second one for detecting crowd convergence and divergence events.
These templates are analyzed using two classifiers.
Efficacy of the proposed methods is studied by using two neural classifiers: a neuro-fuzzy controller (NFC) and a feed-forward back-propagation multi-layered perceptron (MLP) to discriminate between 31 classes of leaves.
The histogram bins are produced for every classifier by using four shifts of the decision boundary in Fig. 2.
The ANN classifier is designed by using two layer feedforward backpropagation neural networks.
The application of the system using three individual classifiers achieved better performance than its application with only two individual classifiers.
We ran the experiments 50 times for each combination of feature selection and learning classifiers, with the feature numbers from 5 to 100. Figure 1 shows the average testing accuracy by using the seven classifiers for the feature sets chosen by GLGS and SVMRFE, with the feature numbers from 5 to 100.
To ensure that the 34 miRNA composition of our model was optimal we repeated the entire analysis using four additional classifiers built by using 5, 10, 15, 30 miRNAs of the initial list of 34 miRNAs (Fig S5).
Figure 4 Performance of classifiers: (a) F-measure when using all features except for P, (b) F-measure enhancement by including P. Figure 4b shows the F-measure of each classifier using all six features minus the F-measure of the same classifiers using five features (which excludes infection betweenness centrality).
This research uses nine different classifiers per channel for a total of 27 experts.
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