Shapley values for feature selection: The good, the bad, and the axioms

D Fryer, I Strümke, H Nguyen - Ieee Access, 2021 - ieeexplore.ieee.org
The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a
large extent, to a solid theoretical foundation, including four “favourable and fair” axioms for …

[HTML][HTML] Explainable machine learning for project management control

JI Santos, M Pereda, V Ahedo, JM Galán - Computers & Industrial …, 2023 - Elsevier
Project control is a crucial phase within project management aimed at ensuring—in an
integrated manner—that the project objectives are met according to plan. Earned Value …

AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks

WX Shen, Y Liu, Y Chen, X Zeng, Y Tan… - Nucleic Acids …, 2022 - academic.oup.com
Omics-based biomedical learning frequently relies on data of high-dimensions (up to
thousands) and low-sample sizes (dozens to hundreds), which challenges efficient deep …

Inferring feature importance with uncertainties with application to large genotype data

PV Johnsen, I Strümke, M Langaas… - PLOS Computational …, 2023 - journals.plos.org
Estimating feature importance, which is the contribution of a prediction or several predictions
due to a feature, is an essential aspect of explaining data-based models. Besides explaining …

Risk–Reward Share Allocation under Different Integrated Project Delivery Relational Structures: A Monte-Carlo Simulation and Cooperative Game Theoretic Solutions …

R Eissa, M Abdul Nabi, IH El-Adaway - Journal of Construction …, 2024 - ascelibrary.org
Sharing of risks and rewards is considered to be one of the key benefits and principles of
integrated project delivery (IPD). Despite its importance, risk–reward strategies are not …

Beyond cuts in small signal scenarios: Enhanced sneutrino detectability using machine learning

D Alvestad, N Fomin, J Kersten, S Maeland… - The European Physical …, 2023 - Springer
We investigate enhancing the sensitivity of new physics searches at the LHC by machine
learning in the case of background dominance and a high degree of overlap between the …

Towards interpreting ML-based automated malware detection models: A survey

Y Lin, X Chang - arXiv preprint arXiv:2101.06232, 2021 - arxiv.org
Malware is being increasingly threatening and malware detectors based on traditional
signature-based analysis are no longer suitable for current malware detection. Recently, the …

[HTML][HTML] Machine learning investigation of high-k metal gate processes for dynamic random access memory peripheral transistor

N Kwon, JH Bang, WJ Sung, JH Han, D Lee, I Jung… - APL Materials, 2024 - pubs.aip.org
Dynamic random access memory (DRAM) plays a crucial role as a memory device in
modern computing, and the high-k/metal gate (HKMG) process is essential for enhancing …

SARGDV: Efficient identification of groundwater-dependent vegetation using synthetic aperture radar

M Terrett, D Fryer, T Doody, H Nguyen… - arXiv preprint arXiv …, 2020 - arxiv.org
Groundwater depletion impacts the sustainability of numerous groundwater-dependent
vegetation (GDV) globally, placing significant stress on their capacity to provide …

Shapley Value for Shortest Path Routing in Dynamic Networks

A Masimli - 2023 - preprints.org
Transportation is a crucial component of supply chain management, responsible for
delivering goods and services to customers. This paper explores the application of game …