Image de couverture de livre

Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Mehdiyev, Nijat, Majlatow, Maxim, Fettke, Peter
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

From Theory to Practice: Real-World Use Cases on Trustworthy LLM-Driven Process Modeling, Prediction and Automation
par: Pfeiffer, Peter, et autres
Publié: (2025)

Human-Centered Evaluation of an LLM-Based Process Modeling Copilot: A Mixed-Methods Study with Domain Experts
par: Lauer, Chantale, et autres
Publié: (2026)

Assessing the Business Process Modeling Competences of Large Language Models
par: Lauer, Chantale, et autres
Publié: (2026)

Combining Machine Learning and Ontology: A Systematic Literature Review
par: Ghidalia, Sarah, et autres
Publié: (2024)

On the Relationship Between Interpretability and Explainability in Machine Learning
par: Leblanc, Benjamin, et autres
Publié: (2023)

Investigating the Duality of Interpretability and Explainability in Machine Learning
par: Garouani, Moncef, et autres
Publié: (2025)

Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
par: Gryech, Ihsane, et autres
Publié: (2024)

Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
par: Yousif, Yasin, et autres
Publié: (2024)

Explainable Machine Learning for ICU Readmission Prediction
par: de Sá, Alex G. C., et autres
Publié: (2023)

Exploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America
par: Domingos, Matheus Corrêa, et autres
Publié: (2025)

A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning
par: Fakour, Fahimeh, et autres
Publié: (2024)

A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)
par: Abbasi, Mostafa, et autres
Publié: (2024)

Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics
par: Munoz, Cristian, et autres
Publié: (2023)

Interpretable Representations in Explainable AI: From Theory to Practice
par: Sokol, Kacper, et autres
Publié: (2020)

Interpretable Machine Learning in Physics: A Review
par: Wetzel, Sebastian Johann, et autres
Publié: (2025)

Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments
par: Rodriguez, Charlotte, et autres
Publié: (2024)

Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
par: Dembinsky, David, et autres
Publié: (2025)

A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection
par: Wu, Yuandi, et autres
Publié: (2024)

Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning
par: Costilla-Reyes, Omar, et autres
Publié: (2025)

Review Non-convex Optimization Method for Machine Learning
par: Fotopoulos, Greg B, et autres
Publié: (2024)

Diffusion Model for Planning: A Systematic Literature Review
par: Ubukata, Toshihide, et autres
Publié: (2024)

Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis
par: Atzmueller, Martin, et autres
Publié: (2024)

Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
par: Su, Jing, et autres
Publié: (2024)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
par: Krishna, Satyapriya, et autres
Publié: (2022)

Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review
par: Rombach, Alexander Michael, et autres
Publié: (2024)

CON-FOLD -- Explainable Machine Learning with Confidence
par: McGinness, Lachlan, et autres
Publié: (2024)

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
par: Kibria, Md Raisul, et autres
Publié: (2025)

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
par: Devabhakthini, Prathyusha, et autres
Publié: (2023)

GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
par: Amara, Kenza, et autres
Publié: (2022)

Explainability of Machine Learning Models under Missing Data
par: Vo, Tuan L., et autres
Publié: (2024)

A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
par: Saulières, Léo
Publié: (2025)

Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
par: Tulili, Tien Rahayu, et autres
Publié: (2026)

Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities
par: Afifi, Nehal, et autres
Publié: (2025)

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
par: Paterakis, George, et autres
Publié: (2025)

Explaining Machine Learning Predictive Models through Conditional Expectation Methods
par: Ruiz-España, Silvia, et autres
Publié: (2026)

An Accurate and Interpretable Framework for Trustworthy Process Monitoring
par: Wang, Hao, et autres
Publié: (2023)

Predictive Process Monitoring Using Object-centric Graph Embeddings
par: Gherissi, Wissam, et autres
Publié: (2025)

Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
par: Šarčević, Antonia, et autres
Publié: (2026)

Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review
par: Peng, Johnny, et autres
Publié: (2025)

From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
par: Huang, Guangming, et autres
Publié: (2024)