Statistics

    

Consensus in Sequential Wrapper Feature Selection: a Unifying Approach

Authors: L. Martino, G. Villacrés, S. Arcidiacono

Feature selection is a crucial task in statistics and machine learning, with direct implications for model interpretability and computational efficiency. This study introduces aunifying approach that combines the four possible sequential wrapper methods employedfor variable selection, aiming to exploit their complementary strengths. The proposed procedure computes feature relevance scores and, subsequently, integrates the outputs from each sequential wrapper method. The underlying idea is simple and efficient. We test it in a controlled experiment with a known ground truth. The results indicate that the ranking obtained by consensus clearly outperform the individual rankings obtained by the wrapper methods.

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[v1] 2025-10-04 09:38:22

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