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AFs are evaluated individually using a wrapper approach.
Our method is more flexible than the wrapper approach.
This method is based on the wrapper approach with random search strategy for subset channel selection.
A new feature selection algorithm [33] based on the wrapper approach using neural networks.
This method adopted a wrapper approach with a random search strategy for subset channel selection.
This method adopts a wrapper approach with pre-specified subset channel selection depending on experience.
We apply the wrapper approach to select features and evaluate performance iteratively [43].
Other methods also could be applied, such as a wrapper approach with a regression SVM.
However, wrapper approach is not suitable for high-dimensional data as it is computationally expensive.
To improve feature evaluation, we chose a wrapper approach to feature selection [65].
The chapter also discusses the filter approach and the Wrapper approach for feature selection.
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