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EcoShap: Save Computations by only Calculating Shapley Values for Relevant Features
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-7055-2706
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-2590-6661
2024 (English)In: Artificial Intelligence. ECAI 2023 International Workshops: XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part I / [ed] Nowaczyk, Sławomir et al., Cham: Springer, 2024, Vol. 1947, p. 24-42Conference paper, Published paper (Refereed)
Abstract [en]

One of the most widely adopted approaches for eXplainable Artificial Intelligence (XAI) involves employing of Shapley values (SVs) to determine the relative importance of input features. While based on a solid mathematical foundation derived from cooperative game theory, SVs have a significant drawback: high computational cost. Calculating the exact SV is an NP-hard problem, necessitating the use of approximations, particularly when dealing with more than twenty features. On the other hand, determining SVs for all features is seldom necessary in practice; users are primarily interested in the most important ones only. This paper introduces the Economic Hierarchical Shapley values (ecoShap) method for calculating SVs for the most crucial features only, with reduced computational cost. EcoShap iteratively expands disjoint groups of features in a tree-like manner, avoiding the expensive computations for the majority of less important features. Our experimental results across eight datasets demonstrate that the proposed technique efficiently identifies top features; at a 50% reduction in computational costs, it can determine between three and seven of the most important features. © The Author(s) 2024.

Place, publisher, year, edition, pages
Cham: Springer, 2024. Vol. 1947, p. 24-42
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1947
Keywords [en]
Explainable Artificial Intelligence (XAI), Feature Importance, Shapley Value
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52744DOI: 10.1007/978-3-031-50396-2_2Scopus ID: 2-s2.0-85184111581ISBN: 978-3-031-50395-5 (print)ISBN: 978-3-031-50396-2 (electronic)OAI: oai:DiVA.org:hh-52744DiVA, id: diva2:1840492
Conference
International Workshops of the 26th European Conference on Artificial Intelligence (ECAI 2023), Kraków, Poland, 30 September-4 October, 2023
Funder
Swedish Research Council, CHIST-ERA-19-XAI-012Available from: 2024-02-23 Created: 2024-02-23 Last updated: 2024-02-23Bibliographically approved

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Jamshidi, ParisaNowaczyk, SławomirRahat, Mahmoud

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