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A Data-Science Pipeline to Enable the Interpretability of Many-Objective Feature Selection

par Njoku, Uchechukwu U.F.;Abelló, Alberto;Bilalli, Besim;Bontempi, Gianluca
Référence CEUR Workshop Proceedings, 3653, page (83-87)
Publication Publié, 2024-07-01

Article révisé par les pairs

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Résumé :

Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. Given the multi-variate nature of the assessment, which may include objectives (e.g., fairness) unrelated to predictive accuracy, this step is often not straightforward and suffers from the lack of existing tools. For instance, it is common to make use of a tabular presentation of the solutions, which provides little information about the trade-offs and the relationships between objectives over the set of solutions. Adopting a GA-based MOFS with six objectives (number of selected features, balanced accuracy, F1-Score, variance inflation factor, statistical parity, and equalised odds) for two feature selection tasks, this paper illustrates the complex challenge of assessing MOFS results and the need for a methodology to aid and justify the final choice of a solution.