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Here we compare two such methods, pyrosequencing and a phylogenetic array, and evaluate classifications based on two variable 16S rRNA gene regions.
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As early as in 1952, Chernoff [1] used the -divergence to evaluate classification errors.
A variation of F1, namely, Macro-F1, is normally reported to evaluate classification effectiveness on skewed datasets.
However, in the case of simulation we can evaluate classification accuracy using established indices (Ruopp et al. 2008).
The aim of this study was to evaluate classification algorithms based on ASOCT measurements for the detection of gonioscopic angle closure.
The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction.
In this paper, we apply methods from speaking voice emotion recognition to singing voice emotion recognition, evaluate classification performances for the first time, and take an in-depth look at important acoustic features.
To evaluate classification performance, trials were divided into disjoint training and test sets.
70% of trials were randomly selected to train the classifier, and the remaining 30% of trials were used as test trials to evaluate classification performance.
To evaluate classification performance, we used fivefold cross validation [ 31].
We empirically performed threefold cross-validation [ 16] to evaluate classification performance.
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