arXiv AI
Jul 3
Evolutionary Feature Engineering for Structured Data
arXiv:2607. 01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization.
Evolutionary Feature Engineering for Structured Data
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data.
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arXiv Machine Learning
Jun 2
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.
arXiv AI
Jul 28
TopoFE: topology-aware LLM-guided Automated Feature Engineering
arXiv:2607. 23286v1 Announce Type: new Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space.
arXiv AI
Jun 10
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.
arXiv AI
2d ago
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
arXiv:2603.02221v3 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasi...
arXiv AI
Jul 2
EVOTS: Evolutionary Transformer Search for Time Series Forecasting
arXiv:2607. 00154v1 Announce Type: cross Abstract: Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transformer architectures despite substantial variation across tasks and forecasting settings.
arXiv AI
Jul 20
Scaling Time Series Classification via XAI-Driven Data Reduction
arXiv:2607. 15774v1 Announce Type: cross Abstract: Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored.
Hugging Face Trending Papers
3d ago
The Nixtlaverse: An Open-Source Ecosystem for Forecasting
Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitted state, training procedures, and how they paral...
arXiv AI
Jun 8
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
arXiv:2605. 30119v2 Announce Type: replace-cross Abstract: Survival analysis concerns the task of predicting the time until an event occurs.
arXiv Machine Learning
Aug 12
REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting
arXiv:2608. 10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples.
arXiv Machine Learning
Sep 21
LLMs as Feature Engineers for Text-and-Tabular Prediction
The paper presents an iterative framework that uses large language models (LLMs) to automatically extract interpretable, schema‑bound categorical features from unstructured text for use in tabular prediction models. A generator LLM proposes semantic definitions, an extractor LLM materializes the features, and a downstream tabular model evaluates their predictive performance, with error‑driven natural‑language feedback guiding the search. Across three public datasets, the error‑driven loop speeds up feature discovery up to three times and the resulting features outperform any subset when combined with TF‑IDF and dense embeddings, while also providing instance‑level interpretability through SHAP importance rankings and a semantic audit trail.
arXiv Machine Learning
Aug 31
SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.