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Specifically, we test the datasets along each dimension.
With respect to the independent test, the datasets employed should be not included into training datasets as well as no homologous to training datasets.
We used a preestablished association method to test the datasets separately and conducted a combined analysis to test the association accordance between the two datasets.
During 10-fold cross-validation test, the datasets are divided into 10-folds, a model is built with N-1 fold samples and the 10th fold data are treated as unseen data, which is used for the prediction as the testing data.
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The learning by the SVMs in situation II is then used to test the dataset, achieving an accuracy of 94.12 %.
Based on χ-tests the dataset could be divided into three groups for both rRNA genes.
However, by omitting the test dataset, the remaining training dataset for fitting the model becomes unequally spaced data.
In our test, the training dataset has only ∼300 targets.
We also repeated the test using the dataset between the fish species.
There are 70 articles in the train dataset and 140 articles for the test dataset (the formers are included).
10.1186/s13015-015-0062-4 Itestcludatasetstest dallsetestedl tested results, and information of all used tools.
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