Improving the stability of wrapper variable selection applied to binary classification

Wrapper variable selection methods are widely adopted in many applications, among which the design of classifiers. The main problem related to these approaches 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 wrapper variable selection procedures while preserving and possibly improving the classification performance. Moreover three binary classifiers are performed in order to prove the effectiveness of the proposed method.