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In addition, the datasets, which produce higher rates of classification accuracy using the distance-based features, have smaller numbers of data samples, smaller numbers of classes, and lower dimensionalities.
To assess the true error rates of classification, we conducted a manual audit using the treatment records of 1,000 randomly selected patients.
The inclusion of cases with an uncertain condition (i.e. patients characterized as glaucoma suspects) would adversely affect the false positive and negative rates of classification and our evaluation of specificity and sensitivity rates of the classifier.
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The rate of classification following training is greater than 93%.
It is also shown that CLNN has the capability of improving the convergence rate of classification.
Accuracy or correct rate of classification is the efficiency of appropriate classification to the total number of classification tests [19].
These plans estimate the rate of classification errors and set plan parameters to reduce the potential impact of such errors.
However, when the original features are concatenated with the new distance-based features, on average the rate of classification accuracy is improved.
The rate of classification subsequent to the training is more than 92%. Figure 31 shows several face examples, and Table 4 presents experimental results.
Table 9 shows the rate of classification accuracy obtained by naïve Bayes, k-NN, and SVM using the 'original' and '+2D' features, respectively.
The numerical results show that the proposed algorithm gives the good correct rate of classification, and its computing speed is much faster than that of the Fourier series approximation method.
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