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Those 11 features were selected by the stepwise selection of GLM.
Then, features were selected using a forward selection method.
The most relevant features were selected using a sequential forward selection algorithm.
Furthermore, the most relevant features were selected using a sequential forward selection algorithm.
The 11 most frequent features were selected in more than 70 of the 100 bootstrapped selections, and the 9 most frequent ones appeared in more than 90 selections.
It should be pointed out that the features were selected arbitrarily and there was no attempt to optimize their selection.
These features were selected after preliminary tests performed on the training data.
Once salient features were selected, a support vector machine classifier (SVC) was designed.
A pixel-based boosting classifier is then trained, and salient features were selected.
In addition, features were selected by applying the information gain criteria.
After these steps, 43 features were selected to build the model.
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