[2603.19888] Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning

Computer Science > Machine Learning arXiv:2603.19888 (cs) [Submitted on 20 Mar 2026] Title:Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning Authors:Antonis Klironomos, Ioannis Dasoulas, Francesco Periti, Mohamed Gad-Elrab, Heiko Paulheim, Anastasia Dimou, Evgeny Kharlamov View a PDF of the paper titled Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning, by Antonis Klironomos and 6 other authors View PDF HTML (experimental) Abstract:The vast collection of machine learning records available on the web presents a significant opportunity for meta-learning, where past experiments are leveraged to improve performance. Two crucial meta-learning tasks are pipeline performance estimation (PPE), which predicts pipeline performance on target datasets, and dataset performance-based similarity estimation (DPSE), which identifies datasets with similar performance patterns. Existing approaches primarily rely on dataset meta-features (e.g., number of instances, class entropy, etc.) to represent datasets numerically and approximate these meta-learning tasks. However, these approaches often overlook the wealth of past experimental results and pipeline metadata available. This limits their ability to capture dataset - pipeline interactions that reveal performance similarity patterns. In this work, we propose KGmetaSP, a knowledge-graph-embeddings approach that leverages existing experiment data to capture these interactions and improve both...