Abstract
In the field of machine learning, a crucial task is understanding the relative importance of the different input features in a predictive model. There is an approach in the literature whose aim is to analyze the predictive capacity of some features with respect to others. Can we explain a feature of the input space with others? Can we quantify this capacity? We propose a practical approach for analyzing the importance of features in a model and the explanatory capacity of some features over others. It is based on the adaptation of existing definitions from the literature that use the Shapley value and fuzzy measures. Our new approach aims to facilitate the understanding and application of these concepts by starting from a simple idea and considering well known methods. The main objective of this work is to provide a useful and practical approach for analyzing feature importance in real world cases.
Supported by the Government of Spain, Grant Plan Nacional de I+D+i, PID2020-116884GB-I00, PID2021-122905NB-C21.
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Gutiérrez, I., Santos, D., Castro, J., Hernández-Gonzalo, J.A., Gómez, D., Espínola, R. (2023). Machine Learning and Fuzzy Measures: A Real Approach to Individual Classification. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_12
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