where C∗(i) represents any collection of classification probabilities for the i th syllable, and n is the range parameter controlling the extent of the context.
Under the random forest approach, observations are considered similar if they tend to converge in the same terminal node in a suitably constructed collection of classification and regression trees (Breiman 2001; Liaw and Wiener 2012).
The probability vector P i) for the i th sample is constructed as follows: P ( i ) = [ C ∗ ( i - n ), ⋯, C ∗ ( i - 1 ), C ∗ ( i ), C ∗ ( i + 1 ), ⋯, C ∗ ( i + n ) ], (2) where C∗(i) represents any collection of classification probabilities for the i th syllable, and n is the range parameter controlling the extent of the context.
A Random Forest is a collection of classification trees that are randomized by training on a bootstrap sample of the training data and also using only a subset of M (< N) of the variables.
With KEGG Brite [36], a collection of hierarchical classifications representing the knowledge on various aspects of biological systems, the database possesses a powerful query frontend.
Computational experiments on a collection of benchmark classification problems shows improvement on the original HERF, and other state-of-the-art approaches.
Our results demonstrate that using the SSVM is more effective than the traditional approach of decomposing the problem into a collection of binary classification problems.
The Weka machine learning software [ 22], which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis.
The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis.
We built a random forest model based on a collection of 500 classification trees with each individual tree built from a bootstrap sample of the original 2,107 donor-patient pairs.
