In the supervised learning context, the filter approach for feature selection consists in the selection of the most appropriate variables for any subsequent machine learning algorithm used for the construction of the model.
The methods are mostly based on the correlation concept (in a large sense). They are interesting because they enable to handle quickly high-dimensional data sets. On the other hand, they are questionable because they do not take into account the characteristics of the model (e.g. linear, non-linear) that will be developed from the selected variables.
Keywords: feature selection, filter methods, embedded methods, wrapper methods
Components (Tanagra): CFS FILTERING, FCBF FILTERING, MIFS FILTERING, MODTREE FILTERING, FEATURE RANKING, FISHER FILTERING, RUNS FILTERING, STEPDISC
Slides: Filter methods
Tutorials:
Tanagra tutorials, "Filter methods for feature selection", 2010.
Tanagra tutorials, "Filter methods for feature selection (continuation)", 2010.
