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The best individual split value is reported to be ({text{R}}_{text{test}}^{2}) = 0.657 for SVRM model with eight variables (LOO at the second split of the data).
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A one-third split of the seven million subscribers would yield 2.3 million customers.
In our work, we selected 14 features, representing one-third split of the ranked features using NSL-KDD benchmark dataset.
Table 3 shows the 13 selected features out of the one-third split of the most important features of NSL-KDD dataset using EMFFS method.
Fourteen most important features of the filter methods were determined by presenting one-third split of the ranked features (see Table 2).
The best individual split value is equal to ({text{R}}_{text{test}}^{2}) = 0.998 for ENET model with eight variables including the final two suggested by the authors (LOO at the eighth split of the data).
IG, gain-ratio, chi-square and ReliefF filter methods are used to rank the feature set of the original dataset to create a mutually exclusive subset before selecting one-third split of the ranked features (i.e. 14 features).
Figure 11 show the confusion matrices for the STIP, STIP+BSIF-TOP, iDT, and iDT+BSIF-TOP features respectively, based on the first split of the HMDB dataset (both HMDB-LQ and HMDB-MQ included).
Filter feature selection methods present different ranking algorithms; therefore, we propose an EMFFS method that combines the output of IG, gain ratio, chi-squared and ReliefF to find common features in the one-third split of the ranked features using NSL-KDD benchmark dataset in the Weka.
The fifth split of the network yielded groups of almost equal size: C5 and C6.
The first split of the network produced two groups of unequal size.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com