CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization
arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within rigid search spaces and lack domain awareness. While Large Language Models (LLMs) offer a promising alternative to generate unbounded operators with semantic reasoning, existing methods focus on isolated subtasks such as feature generation, falling short of free-form FE pipelines. Moreover, they are rarely coupled with hyperparameter optimization (HPO) of the downstream ML model, leading to greedy "
The proliferation of powerful LLMs and the need to automate and optimize the complex feature engineering bottleneck in AutoML pipelines are converging, making such solutions both feasible and necessary now.
This work represents a significant step towards fully autonomous and highly optimized machine learning pipelines, reducing the human expert burden and increasing the efficiency and performance of AI systems across various domains.
Traditional, human-intensive feature engineering processes are now directly challenged by LLM-driven automation, which can generate and optimize features more creatively and integrate with hyperparameter optimization.
- · AI/ML developers
- · AutoML platforms
- · Industries deploying AI
- · LLM providers
- · Human feature engineering specialists
- · Legacy AutoML tools
- · Manual ML pipeline optimization
Automated machine learning pipelines become significantly more efficient, requiring less human intervention and achieving higher performance.
The cost and time associated with deploying sophisticated AI models decrease, accelerating AI adoption and innovation across diverse applications.
A new class of AI systems emerges, capable of full self-optimization from raw data to deployed model, leading to unforeseen applications and productivity gains.
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