Method | Description | Examples |
---|---|---|
Filter | Filter methods evaluate the relatedness of features by looking at the inherent properties of the data. Usually a feature relevance score is computed, and the features with low scores are discarded. | Student’s t-test [N/A] |
Information gain [38] | ||
Gain ratio [38] | ||
Chi squared [N/A] | ||
Symmetrical uncertainty [39] | ||
Unbalanced correlation score [40] | ||
Mann–Whitney test [41] | ||
Linear correlation coefficient [N/A] | ||
Wrapper | In wrapper methods various subsets of features are evaluated by training and testing a specific classification model, so a search algorithm is ‘wrapped’ around the classification model. This approach adapted to a specific classification algorithm. | Sequential forward selection [42] |
Sequential backward elimination [42] | ||
Beam search [43] | ||
ReliefF [44] | ||
Embedded | Embedded methods, build the search for an optimal subset of features into the classifier construction, so they are specific to a given learning algorithm. | Random forest [45] |
SVM recursive feature elimination (SvmRfe) [46] | ||
One attribute rule [47] |