Hybrid methods
Starting with 435 features, there are over 1042 combinations of 27 feature subsets alone! So, you can see how EFS would be impractical on such a large feature space. Therefore, except for EFS on the entire dataset, wrapper methods will invariably take some shortcuts to select the features. Whether you are going forward, backward, or both, as long as you are not assessing every single combination of features, you could easily miss out on the best one.
However, we can leverage the more rigorous, exhaustive search approach of wrapper methods with filter and embedded methods' efficiency. The result of this is hybrid methods. For instance, you could employ filter or embedded methods to derive only the top-10 features and perform EFS or SBS on only those.
Recursive feature elimination
Another, more common approach is something such as SBS, but instead of removing features based on improving a metric alone, using the model's intrinsic parameters to rank the features...
