Feature selection is similar to:

Explanation: Data preprocessing needs basic intuition to be done, while feature selection comprehensive understanding of the data and statistics.

While dimensionality reduction and feature selection are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension. It is for creating a new, smaller set of features that still captures most of the useful information. Again, feature selection keeps a subset of the original features while dimensionality reduction creates new ones.