Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 2144-2155· 0 citations· 4 references
Abstract
In domains such as finance, healthcare, and industry, feature engineering remains the key bottleneck limiting the performance of machine learning models on tabular data. While Automated Feature Engineering (AutoFE) aims to reduce this manual effort, existing approaches still suffer from distinct limitations: data-driven exploration can waste substantial computation on semantically meaningless feature combinations, whereas knowledge-driven approaches using Large Language Models (LLMs) struggle to construct high-order interactions without rich structural context and are typically guided only by coarse global metrics. We propose CoFE (Collaborative Feature Engineering), a two-phase framework that tightly couples search-based exploration with LLM-driven reasoning. In the exploration phase, CoFE leverages an LLM-constructed semantic feature schema to guide a Monte Carlo Tree Search (MCTS), enforcing semantic constraints while encouraging a diverse pool of complex candidate features that provides the missing structural context for LLMs. In the refinement phase, CoFE introduces feature health reports, a diagnostic artifact that supplies the LLM with actionable sample-level and structural feedback for targeted corrections. Experiments on 16 public tabular benchmarks show that CoFE consistently outperforms state-of-the-art data-driven and LLM-based AutoFE methods on the majority of datasets, while offering favorable computational efficiency.
Experimental evaluation on the Madelon dataset indicates that the automated and interpretable pipeline performs comparably to, and in some respects favourably against, baseline feature engineering approaches, demonstrating the practical effectiveness of combining scalable feature generation with interpretable AutoML.
CH. Vasavi, SK. Raqeeba· International Journal for Re...· 0 citations
Automated feature engineering with large language models (LLMs) can produce semantically meaningful features for tabular data, yet existing methods lack structured domain knowledge, rigorous verification, and explainable provenance. We propose KnowFeat, a knowledge-guided feature engineering framework that organizes domain knowledge into five types -- schema metadata, regulatory indicators, detection rules, expert opinions, and court document evidence -- and injects them as structured context into an LLM agent. A three-stage verification pipeline filters candidates through code execution, statistical quality checks, and model effectiveness evaluation. Every accepted feature carries a provenance record tracing its design to specific knowledge assets. Under a strict held-out protocol that eliminates feature-selection leakage, KnowFeat ranks first (avg. rank 2.3) across twelve public benchmarks among seven methods (one-sided Wilcoxon p=0.017), with a peak gain of +11.6 pp AUC on a telecom churn dataset. On a real-world Bitcoin anti-money laundering (AML) dataset (Elliptic) and a synthetic digital currency AML benchmark (SimECNY), KnowFeat maintains competitive detection performance with full provenance traceability.
Chengsong You, Wangyue Li, Wei-Qiao Que et al.· 0 citations
Traditional automated feature engineering (AFE) minimizes human intervention but often neglects semantic relationships among original features, resulting in redundant or uninterpretable transformations. While recent methods leverage the domain knowledge and reasoning capabilities of Large Language Models (LLMs), they typically constrain the search space via predefined mathematical operators or suffer from poor exploration-exploitation balance due to exclusive reliance on validation feedback. To address these limitations, we propose MORE-FE, an evolutionary AFE framework that integrates multi-operator exploration with Reinforcement Learning with Verifiable Rewards (RLVR). MORE-FE treats feature transformation programs as hypotheses and evolves them using evolutionary operators that explore semantically coherent and logically structured features through various prompting strategies. Moreover, it employs RLVR to align LLM reasoning with structured evolutionary exploration using a composite reward that balances quality and diversity. Extensive experiments on multiple classification and regression datasets demonstrate that MORE-FE consistently outperforms state-of-the-art AFE methods, highlighting the effectiveness of balancing quality and diversity in feature engineering.
Chang-Yu Chao, Bryan Andersen, Xiao-Xi Tan et al.· Proceedings of the 32nd ACM...· 0 citations
While general-purpose large language models (LLMs) demonstrate remarkable capabilities, their clinical application demands rigorous adaptation to ensure safety and accuracy. This review presents a comprehensive framework for transforming LLMs into trustworthy medical specialists. We detail three core knowledge-injection strategies-(1) static embedding to internalize foundational biomedical knowledge; (2) behavioral alignment to enforce clinical safety and verifiable diagnostic logic; and (3) dynamic injection, such as retrieval-augmented generation, for real-time evidence grounding-together with multimodal integration as a complementary perception-injection paradigm extending the input space beyond text to imaging, biosignals, and tabular data. Building on these strategies, we further explore the evolution toward agentic AI systems that orchestrate them for autonomous, collaborative clinical decision-making. Finally, we discuss critical challenges, including model calibration, resource constraints, standardized reporting, and robust safety protocols. Combining these complementary strategies is essential for developing deployable, domain-specialized clinical AI systems.
This work proposes AutoMedImg, a multi-agent framework for fully automated medical image processing code generation that achieves zero human intervention, with Dice scores of up to 0.90 for segmentation tasks and 99% accuracy for classification.
Zixiao Zhao, Jing Sun, Zhe Hou et al.· 0 citations
This entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.
Qiao Jin, Nicholas Wan, Robert Leaman et al.· Nature Protocols· 1 citation
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