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Because of the different classification criteria, regions are usually with spatial disagreement.
Based on evaluation results a comparison of the different classification approaches and superordinate basic method groups is performed.
In addition, a multilayer perceptron and support vector machine are used as additional classifiers to compare the results of the different classification schemes.
The construction of the different classification trees has random components i.e., for each tree, only a random subset of observations, and for each decision node, only a random subset of features is considered leading to the term Random Forests.
The feature measure that is used by the feature selection method is independent of any classification algorithm, thus allowing us to compare the performances of the different classification algorithms.
The agreement of the different classification methods was calculated using the kappa statistic.
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*denote P < 0.05 for prevalence of undernutrition based on the different classifications across baseline characteristics using χ test.
Table 2 shows the cross-tabulated distribution of patients according to the different classifications used.
The relative proportions of the different classifications of refractive error for all children combined (including those of unknown gender) for each age group are shown in Table 2.
Figure 2 summarizes the results of comparison between the different classification methods.
We compared the accuracy of the prediction by using the different classification methods implemented in BRB-Array tools.
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