Results 31 to 40 of about 2,644 (209)
CoRR, 2020 Shapley value is a concept in cooperative game theory for measuring the contribution of each participant, which was named in honor of Lloyd Shapley. Shapley value has been recently applied in data marketplaces for compensation allocation based on their contribution to the models.
openaire +2 more sources
Leak Localization Using Autoencoders and Shapley Values
Engineering ProceedingsThis study outlines the use of a game theoretic approach for preliminary leak localization in water distribution networks. The proposed method consists of an autoencoder model at its core, trained to reconstruct input pressure signals recorded during ...
Prasanna Mohan Doss +2 more
doaj +1 more source
Variance Allocation and Shapley Value [PDF]
Methodology and Computing in Applied Probability, 2017 20pages
COLINI BALDESCHI, RICCARDO +2 more
openaire +3 more sources
Two Approaches to Estimate the Shapley Value for Convex Partially Defined Games
Mathematics, 2023 In the classical approach of von Neumann and Morgenstern to cooperative games, it was assumed that the worth of all coalitions must be given. However, in real-world problems, the worth of some coalitions may be unknown.
Satoshi Masuya
doaj +1 more source
Integrating traditional and non-traditional model risk frameworks in credit scoring
South African Journal of Economic and Management SciencesBackground: An improved understanding of the reasoning behind model decisions can enhance the use of machine learning (ML) models in credit scoring.
Hendrik A. du Toit +2 more
doaj +1 more source
Scientific Reports, 2021 The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports.
Siyi Tang +6 more
doaj +1 more source
The interval Shapley value: an axiomatization
Central European Journal of Operations Research, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
S. Z. Alparslan Gök +2 more
openaire +5 more sources
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Understanding interpretable patterns of Shapley behaviours in materials data
Machine Learning. EngineeringExplainable artificial intelligence (XAI) techniques are increasingly becoming integrated into many scientific workflows. However, the involvement of such methods typically manifests in the later stages of the workflow such as in model fitting and ...
Tommy Liu, Amanda S Barnard
doaj +1 more source
Advanced Functional Materials, EarlyView.A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao +6 more
wiley +1 more source