Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning

1. 1Extract meta-features from past machine learning experiments
2. 2Integrate meta-features with knowledge graph embeddings to capture complex relationships
3. 3Apply the integrated model to pipeline performance estimation (PPE) and dataset performance-based similarity estimation (DPSE) tasks
4. 4Evaluate the performance of the integrated model against baseline models

Data scientists and machine learning engineers on a team can benefit from this research as it enhances the accuracy of meta-learning models, which can inform better decision-making and model selection

💡 Combining meta-features with knowledge graph embeddings can significantly enhance the accuracy of meta-learning models

🚀 Meta-learning just got a boost! Integrating meta-features with knowledge graph embeddings improves pipeline performance estimation and dataset similarity estimation