You are given a dataset with a high number of features. The computational resources are limited. What feature selection method might you consider?

Given limited computational resources, filter methods might be a good choice. These methods are less computationally expensive than wrapper methods as they do not involve the use of any specific machine learning algorithm. Instead, they rank features based on statistical measures and remove irrelevant features based on a certain threshold or number of top features to keep.