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Different instances of ELM were fed with features selected from different bootstrap samplings of the real-life field datasets.
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Bagging (which stands for Bootstrap Aggregation) consists of an ensemble (or set) of decision trees, where different decision trees in the ensemble are produced by different random samplings (different bootstrap samples) of the original training set [31].
The robustness of inferred topologies was tested by 1000 bootstrap re-samplings of the neighbor-joining data.
We estimate the confidence intervals for the diversity measurements via 1000 bootstrap re-samplings of the dataset.
The standard error of the de-attenuated coefficient was calculated with bootstrap sampling (ten samples) in the calibration models to take into account the uncertainty related to measurement error correction.
The numbers above the branches indicate the percentage of bootstrap samplings.
A total of 100 non-parametric bootstrap samplings were performed to estimate the support level for each internal branch in the ML, NJ and MP trees.
A total of 100 non-parametric bootstrap samplings were carried out to estimate the support level for each internal branch for both the ML and NJ trees.
Bootstrap scores of ML tree were calculated by running 100 bootstrap samplings.
Bootstrap support values were calculated using 1000 samplings of the dataset [ 25] to estimate statistical reliability for MP, NJ, and ML methods.
Bootstrap values are indicated at the branching points (percent values from 500 replicate bootstrap samplings).
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