Imagen de Portada

SHAP-Guided Regularization in Machine Learning Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Saadallah, Amal
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Explainable Adaptive Tree-based Model Selection for Time Series Forecasting
por: Jakobs, Matthias, et al.
Publicado: (2024)

Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting
por: Saadallah, Amal, et al.
Publicado: (2025)

SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning
por: Li, Na, et al.
Publicado: (2025)

A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
por: Saadallah, Ons, et al.
Publicado: (2026)

Research on Milling Machine Predictive Maintenance Based on Machine Learning and SHAP Analysis in Intelligent Manufacturing Environment
por: Zhao, Wen, et al.
Publicado: (2025)

REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values
por: Sharma, Shubham, et al.
Publicado: (2024)

Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning
por: Belahcen, Adam, et al.
Publicado: (2026)

Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions
por: Wettenstein, Ron, et al.
Publicado: (2026)

Interpretable Credit Default Prediction with Ensemble Learning and SHAP
por: Yang, Shiqi, et al.
Publicado: (2025)

The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
por: Cifuentes, Santiago, et al.
Publicado: (2024)

TabSHAP
por: Chaudhary, Aryan, et al.
Publicado: (2026)

PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression
por: Fumagalli, Fabian, et al.
Publicado: (2026)

Interaction Tensor SHAP
por: Hasegawa, Hiroki, et al.
Publicado: (2025)

Improving the Weighting Strategy in KernelSHAP
por: Olsen, Lars Henry Berge, et al.
Publicado: (2024)

Enhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification
por: de Oliveira, Filipe Ferreira, et al.
Publicado: (2025)

Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
por: Whitehouse, Justin, et al.
Publicado: (2026)

A Method for Evaluating the Interpretability of Machine Learning Models in Predicting Bond Default Risk Based on LIME and SHAP
por: Zhang, Yan, et al.
Publicado: (2025)

InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly
por: Enouen, James, et al.
Publicado: (2025)

Towards trustable SHAP scores
por: Letoffe, Olivier, et al.
Publicado: (2024)

SHAP values via sparse Fourier representation
por: Gorji, Ali, et al.
Publicado: (2024)

CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better Interpretability of Intelligent Fault Diagnosis
por: Chen, Qian, et al.
Publicado: (2025)

Differentiated Thyroid Cancer Recurrence Classification Using Machine Learning Models and Bayesian Neural Networks with Varying Priors: A SHAP-Based Interpretation of the Best Performing Model
por: Kumari, HMNS, et al.
Publicado: (2025)

ContextualSHAP : Enhancing SHAP Explanations Through Contextual Language Generation
por: Dwiyanti, Latifa, et al.
Publicado: (2025)

ConfoundingSHAP: Quantifying confounding strength in causal inference
por: Brockschmidt, Marie, et al.
Publicado: (2026)

RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
por: Chowdhury, Tanya, et al.
Publicado: (2024)

Explanation Multiplicity in SHAP: Characterization and Assessment
por: Hwang, Hyunseung, et al.
Publicado: (2026)

Knowledge-Guided Machine Learning Models to Upscale Evapotranspiration in the U.S. Midwest
por: Rozanov, Aleksei, et al.
Publicado: (2025)

Replication Study: Enhancing Hydrological Modeling with Physics-Guided Machine Learning
por: Esmaeilzadeh, Mostafa, et al.
Publicado: (2024)

A review on development of eco-friendly filters in Nepal for use in cigarettes and masks and Air Pollution Analysis with Machine Learning and SHAP Interpretability
por: Paneru, Bishwash, et al.
Publicado: (2025)

Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
por: Guillén, Pablo, et al.
Publicado: (2026)

Can Global XAI Methods Reveal Injected Bias in LLMs? SHAP vs Rule Extraction vs RuleSHAP
por: Sovrano, Francesco
Publicado: (2025)

Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction
por: Clement, Tobias, et al.
Publicado: (2024)

Explainable time-series forecasting with sampling-free SHAP for Transformers
por: Hertel, Matthias, et al.
Publicado: (2025)

A comparative analysis of machine learning models in SHAP analysis
por: Lin, Justin, et al.
Publicado: (2026)

Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
por: Abubakkar, Md, et al.
Publicado: (2026)

High-order Regularization for Machine Learning and Learning-based Control
por: Liu, Xinghua, et al.
Publicado: (2025)

$ϕ$-Table: A Statistical Explanation for Global SHAP
por: Kim, Dongseok, et al.
Publicado: (2025)

Lightweight Intrusion Detection in IoT via SHAP-Guided Feature Pruning and Knowledge-Distilled Kronecker Networks
por: Benaddi, Hafsa, et al.
Publicado: (2025)

SHAP-based Explanations are Sensitive to Feature Representation
por: Hwang, Hyunseung, et al.
Publicado: (2025)

From SHAP Scores to Feature Importance Scores
por: Letoffe, Olivier, et al.
Publicado: (2024)