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Committee Date Time Place Paper Title / Authors Abstract Paper #
ICSS, IPSJ-SPT 2024-03-22
11:20		 Okinawa OIST
(Primary: On-site, Secondary: Online)		 Evaluation of Feature Inference Risk from Explainable AI metrics LIME and Shapley Values
Ryotaro Toma, Hiroaki Kikuchi (Meiji Univ.) ICSS2023-88		 Explainability has gained attention to ensure fairness and transparency in machine learning models, providing users with... [more] ICSS2023-88
pp.137-144
DC 2024-02-28
16:05		 Tokyo Kikai-Shinko-Kaikan Bldg. DC2023-103 This study focuses on interdependent networks.
The objective of this study is to solve the problem of selecting nodes... [more]		 DC2023-103
pp.53-58
IBISML 2022-12-23
15:30		 Kyoto Kyoto University
(Primary: On-site, Secondary: Online)		 Finding of Corporate Growth Patterns by Shapley Values -- Case Study of Academy Development in the J-League --
Seiji Matsuhashi, Yukari Shirota (Gakushuin Univ) IBISML2022-64		 This paper explores the financial indicators (variables) that contribute to the J-league ranking of a club by regression... [more] IBISML2022-64
pp.150-157
DE 2022-06-25
10:05		 Tokyo Musashino University
(Primary: On-site, Secondary: Online)		 Time Series Analysis of Shapley Values in Machine-Learning Regression
Kotaro Kuno, Yukari Shirota (GakushuinUniv) DE2022-1		 In regression analysis of machine learning, Lundberg's SHAP and its libraries are widely used and have contributed great... [more] DE2022-1
pp.1-6
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