Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Description
The 5-volume set CCIS 2839 - 2843 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, which took place in Porto, Portugal, in September 2025.
The 236 full papers included in these proceedings were carefully reviewed and selected from 413 submissions. The papers were organized topical sections as follows:
Part I: Workshop on Data Science for Social Good SoGood 2025), Workshop on Bias and Fairness in AI (BIAS 2025), Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2025), Human-Centered Data Mining Workshop (HuMine 2025) and Workshop on Data-Centric Artificial Intelligence (DEARING 2025).
Part II: Workshop on Hybrid Human-Machine Learning and Decision Making (HLDM 2025), Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2025), Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2025),Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2025), Workshop on Deep Learning Meets Neuromorphic Hardware (DLmNH 2025), Machine Learning for Cybersecurity (MLCS 2025),AI for Safety-Critical Infrastructures (AI-SCI 2025) and Workshop on Innovations, Privacy-preservation, and Evaluations of Machine Unlearning Techniques (WIPE-OUT).
Part III: Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025), Workshop on Synthetic Data for AI Trustworthiness and Evolution (SynDAiTE 2025), Workshop on MIning Data for Financial Applications (MIDAS 2025), Workshop on Advancements in Federated Learning (WAFL 2025) and Workshop on Mining and Learning with Graphs (MLG 2025).
Part IV: Workshop on Interactive Adaptive Learning (IAL 2025), Workshop on Machine Learning for Irregular Time Series (ML4ITS 2025), Interactive eXplainable AI, Theory and Practice (IXAIT 2025), Workshop on Learning on Real and Synthetic Medical Time Series Data (MED-TIME 2025), Workshop on Responsible Healthcare Using Machine Learning (RHCML 2025), Workshop for Explainable AI in Time Series and Data Streams (TempXAI 2025) and Workshop on Explainable Knowledge Discovery in Data Mining and Unlearning (XKDD 2025).
Part V: Workshop on Learning from Small Data (LFSD 2025), Workshop on Machine Learning for Earth Observation (MACLEAN 2025), Workshop on Artificial Intelligence, Data Analytics and Democracy (AIDEM 2025) and Discovery Challenges.
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Content
.- Workshop on Data Science for Social Good SoGood 2025)
.- Detecting Suspicious Activities in Waste Transport Data via Temporal and Network-Aware Change Detection.
.- Automated Fish Size Measurement System for Long-Term Growth Studies in the Azores.
.- Enabling Semi-Supervised Travel Mode Prediction Through Synthetic Unlabelled Trip Instances.
.- Deep Learning for Food Security Forecasting in West and East Africa.
.- Towards Responsible AI Governance: A Multidimensional Ethical Evaluation Framework.
.- Are Companies Taking AI Risks Seriously? A Systematic Analysis of Companies' AI Risk Disclosures in SEC 10-K forms.
.- Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal.
.- False Information on COVID-19: Scoping Review and Future Research Directions.
.- Generating Experimental Area-Level Synthetic Income Datasets for use in Poverty Research.
.- Modelling Alcohol Mortality via Machine Learning and Retail Behavioural Data.
.- Influencing YouTube Recommendations Through Shared Links.
.- Text Standardisation in Public Salary Disclosure Data.
.- Workshop on Bias and Fairness in AI (BIAS 2025)
.- A Representation-Level Assessment of Bias Mitigation in Foundation Models.
.- No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models.
.- An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case.
.- Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback.
.- Assessing Trustworthiness of AI Training Dataset using Subjective Logic - A Use Case on Bias.
.- Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters.
.- Counterfactual Fairness with Graph Uncertainty.
.- How Much Does Cluster Fairness Cost? A Counterfactual-based Approach.
.- Post Hoc Fairness Audit of Algorithmic Hiring: A Case Study from the Italian Labor Market Post Hoc Fairness Audit of Algorithmic Hiring: A Case Study from the Italian Labor Market.
.- Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2025) :
.- Few-Shot Knowledge Graph Question Generation via LLM Abstraction-to-Instantiation Conversion and Small Models Collaboration.
.- Evaluation of Knowledge Graph Construction Methods on the Stroke Domain.
.- Unveiling Misinformation Spread with Graph Neural Networks: a Model-Driven Explainable Approach.
.- On the Role of Embeddings in Diffusion-based Generation of scRNA-seq Data.
.- Understanding the Success of Semi-Supervised Learning: A Case Study of Mitotic Phase Classification Using Raman Imaging.
.- Effective Image Representations for Tree Pollen Recognition.
.- LLM-Assisted Topic Reduction for BERTopic on Social Media Data.
.- Fine-tuning foundation models for temporal knowledge graph reasoning.
.- Conversational Knowledge Extraction from Technical Manuals: An LLM-based Framework with Ontological Guidance.
.- Classification of Internet Traffic: A Distributional Data Approach.
.- Human-Centered Data Mining Workshop (HuMine 2025)
.- Trustworthiness and Medical Usefulness of Explainability Techniques in ML-Supported Depression Screening within Primary Care.
.- Workshop on Data-Centric Artificial Intelligence (DEARING 2025)
.- Reusing a BigEarthNet Deep Model to Map Bark Beetle Outbreaks in Sentinel-2 Forest Images.
.- Multi-Agent Reinforcement Learning for Financial Market Trading: An Expert-System Approach.
.- Demographic-Agnostic ECG Biometrics: Addressing Bias in Deep Learning-Based Personal Identification.
.- Initial Demand Prediction for New Fashion Products in the Fast Fashion Industry: Addressing Lost Sales.
.- Domain-constrained Data Augmentation for Entity Resolution.
