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However, standard wrapper-based methods do not optimize the size of optimal feature subset.
To find an optimal feature subset, we first used Boruta algorithm39 to rank features.
The optimal feature selection method retains the useful features and discards the redundant features.
We explore the relation between optimal feature subset selection and relevance.
The results illustrate that deep neural networks can effectively learn the optimal feature representation from materials composition without any need for manual feature engineering using domain knowledge.
Another hybrid prediction model helps in producing optimal feature subset.
An optimal feature set was identified with recursive feature selection and cross-validations.
The mesh routers must be synchronized [27] as it is the optimal feature of WMN.
Furthermore, it is computationally infeasible to select the optimal feature subset by exhaustive search.
After the optimal feature subset has been selected, classification is done on the test data set.
The optimal feature subset selected from development dataset improves the performance on the test dataset.
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