I am searching for a feature selection algorithm which selects features that are:
- relevant to discriminate groups of samples (for each sample a group label is provided)
- endowed with high variance across all the samples
This should be applied to gene expression dataset, in which each sample has a group label, therefore it should be possible to select for each group a set of features to be checked against.
I have now two candidates:
- selecting features by the feature importance result of a Random Forest classifier
- using the Minimum Redundancy Maximum Relevance (mRMR) algorithm
However, I am unsure of which may be the best or if there are better candidates for this purpose.
If the algorithm is implemented in Python scikit-learn it would be a plus.