A preprocessing Shapley value-based approach to detect relevant and disparity prone features in machine learning
Decision support systems became ubiquitous in every aspect of human lives. Their reliance
on increasingly complex and opaque machine learning models raises transparency and …
on increasingly complex and opaque machine learning models raises transparency and …
Shapley value-based approaches to explain the quality of predictions by classifiers
GD Pelegrina, S Siraj - IEEE Transactions on Artificial …, 2024 - ieeexplore.ieee.org
The use of algorithm-agnostic approaches for explainable machine learning is an emerging
area of research. When explaining the contribution of features towards the predicted …
area of research. When explaining the contribution of features towards the predicted …
Notes on Information Propagation in Noisy Multichannel Data Models: Insights into Sensor Selection and Fusion in Multimodal Biomedical Applications
R Sameni - arXiv preprint arXiv:2312.15725, 2023 - arxiv.org
Multimodality and multichannel monitoring have become increasingly popular and
accessible in engineering, Internet of Things, wearable devices, and biomedical …
accessible in engineering, Internet of Things, wearable devices, and biomedical …
A preprocessing Shapley value-based approach to detect relevant and disparity prone features in machine learning
Decision support systems became ubiquitous in every aspect of human lives. Their reliance
on increasingly complex and opaque machine learning models raises transparency and …
on increasingly complex and opaque machine learning models raises transparency and …
Enhanced subsurface estimation and uncertainty modeling through data science and engineering physics
JL Hernandez Mejia - 2024 - repositories.lib.utexas.edu
Subsurface characterization is crucial for the exploration, development, and management of
energy resources, providing essential information about geological formations. However …
energy resources, providing essential information about geological formations. However …