As Aditya said, there are 3 feature-related terms that sometimes are confused with each other. I will try and give summary explanation to each one of them:
- Feature extraction: Generation of features from data that are in a format that is difficult to analyse directly/are not directly comparable (e.g. images, time-series, etc.) In the example of a time-series, some simple features could be for example: length of time-series, period, mean value, std, etc.
- Feature transformation: Transformation of existing features in order to create new ones based on the old ones. A very popularly used technique for dimensionality reduction is Principal Component Analysis (pca) that uses some orthogonal transformation in order to produce a set of linearly non-correlated variables based on the initial set of variables.
- Feature selection: Selection of the features with the highest "importance"/influence on the target variable, from a set of existing features. This can be done with various techniques: e.g. Linear Regression, Decision Trees, calculation of "importance" weights (e.g. Fisher score, ReliefF)
If the only thing you want to achieve is dimensionality reduction in an existing dataset, you can use either feature transformation or feature selection methods. But if you need to know the physical interpretation of the features you identify as "important" or you are trying to limit the amount of data that need to be collected for your analysis (you need all the initial set of features for feature transformation), then only feature selection can work.
You can find more details on Feature Selection and Dimensionality Reduction in the following links:
