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Through 10-fold cross-validation, the performance of all five machine learning algorithms shows that Random Forest classifier can obtains the best prediction performance with the highest accuracy over 0.73 among all five and followed by Decision Tree (Table 1 and Additional file 1: Table S1).
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Values were converted to a scale of 0 1.0 and then used in the decision tree (Table 2).
The features were classified by decision tree algorithms.
A method for splitting the decision tree hash table is discussed that allows the hash table to be updated and accessed simultaneously without the use of locks.
Our method tends to give fewer rules than that obtained by decision-tree based methods.
The classification accuracy of the various weighting schemes is compared with that for the decision tree method in Table 7.
Specifically, we used the full ECBDL14 dataset to show in Table 6 the true rates for the positive and negative classes obtained by the decision tree in the test partitions.
Finally, the classification was performed by a decision tree.
Hence the effect of number of features was studied by using decision tree.
Interactions can naturally be captured by the decision tree structure.
We converted the OR-decision table (Table 3) into a decision tree directly, without converting the OR-decision table into a single action decision table, using the algorithm previously reported in [21].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com