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Improving the stability of Sequential Forward variables selection

Sequential Forward Selection is one of the most popular variable selection methods and is widely adopted in many applications, among which the design of classifiers. The main problem related to this approach regards the stability of the selection, namely the exploitation of different training data set can lead to the selection of different variable subsets. This problem is particularly critical in applications where variable selection is used to interpret the behaviour of the process or phenomenon under consideration, i.e. to understand which among a potentially huge list of variables actually affect the classification. The paper proposes a method that improves the stability of the sequential forward selection procedure while preserving and possibly improving the classification performance.