Automated Feature Engineering

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Automated feature engineering studies how to create, transform, and select useful inputs for machine-learning models, especially when raw tables, sequences, text, or relational data need task-specific representations. Methods range from operator search, genetic programming, reinforcement learning, and symbolic regression to LLM agents that write and test executable feature programs, often using model feedback, expert knowledge, or causal structure. Work evaluates these features on tabular benchmarks and domain tasks such as clinical prediction, forecasting, anomaly detection, and algorithm selection, with growing attention to interpretability and deployment cost. A central challenge is balancing predictive gains with search efficiency, leakage prevention, robustness to distribution shifts, and transfer across datasets and model classes.

Latest papers

54 papers6 papers in the last four weeks, down 14% on the four weeks before. 0.0% of all new papers.

Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search
Oct 8, 2026Ziming Dai, Dabiao Ma, Ziheng Guo +2Automated Feature Engineering Large Language Model-Based Feature Engineering
City University of Hong Kong Hong Kong, China
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation
Oct 8, 2026Alessio Pellegrino, Jacopo MauroAlgorithm Selection Constraint Programming
University of Southern Denmark, Denmark
AutoDP-LLM: Automating Data Pre-processing for Intrusion Detection Systems using Large Language Models
Oct 4, 2026Bao-Phong Nguyen, Gia-Khanh Pham, Thai-Duong Do +7Network Intrusion Detection LLMs for Cybersecurity
Business AI Lab, College of Technology, National Economics University, Hanoi, Vietnam
Evolving LLM-Generated Features for Interpretable Classification
Oct 2, 2026Jack Butler, Zainab Afolabi, Nikita KozodoiInterpretable ML Automated Feature Engineering
Amazon Web Services
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Sep 29, 2026Deqing Fu, Huangyuan Su, Rajat Sen +4Tabular Prediction Automated Machine Learning
Google Research · University of Southern California · Google DeepMind +2
Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting
Sep 28, 2026Nasser AlkhulaifiAutomated Machine Learning Electricity Load Forecasting
School of Computer Science, Faculty of Science University of Nottingham
Flat-Consensus Diffusion for Robust Data Reshaping under Noisy Evaluator
Sep 26, 2026Hongyu Cao, Kunpeng Liu, Fei Xie +1Automated Feature Engineering Diffusion Guidance
Clemson University Clemson, SC, USA · Portland State University Portland, OR, USA · University of Florida Gainesville, FL, USA
Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation
Sep 24, 2026Zhaoyang Cao, Miriam Metzger, Reza ZafaraniFake News Detection Computational Social Science
Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA · Department of Communication, University of California, Santa Barbara, Santa Barbara, CA, USA
LLMs as Feature Engineers for Text-and-Tabular Prediction
Sep 18, 2026Merwan Barlier, Blaz SkrljAutomated Feature Engineering Large Language Model-Based Feature Engineering
Teads
LLM-Generated Feature Pools for Time Series Anomaly Detection
Sep 18, 2026Youssef Attia El Hili, Malik Tiomoko, Corinne AncourtLarge Language Model-Based Feature Engineering Time-Series Anomaly Detection
Huawei Noah’s Ark Lab, Paris, France · Centre de Recherche en Informatique, Mines Paris, PSL University
Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV
Sep 14, 2026Mohamad Najafi, Hongyun Fu, Mathias Brochhausen +2Clinical Outcome Prediction Electronic Health Records
Old Dominion University, Virginia, USA · University of Arkansas for Medical Sciences, Arkansas, USA
Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
Sep 9, 2026Rui Liu, Tao Zhe, Yanyong Huang +5Representation Learning Hierarchical Representation Learning
University of Kansas Lawrence, United States · Southwest University of Finance and Economics Chengdu, China · University of Wisconsin–Madison Madison, United States +3
GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks
Sep 8, 2026Yice Luo, Yun Zhu, Xi Chen +7Financial Fraud Detection Feature Selection
Ant Group Hangzhou, China · Nanjing University Nanjing, China
SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Aug 28, 2026Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2Large Language Model-Based Feature Engineering Automated Feature Engineering
National Key Laboratory for Novel Software Technology, Nanjing University · School of Intelligence Science and Technology, Nanjing University · School of Artificial Intelligence, Nanjing University
Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline
Aug 19, 2026Vivek Batra, Kristin Chen, Sanjiv Das +6Financial Forecasting Automated Feature Engineering
Franklin Templeton Investments Mumbai, India · Blend360 Columbia, USA · Santa Clara University Santa Clara, USA +1
SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE
Aug 18, 2026Xuan Zheng, Kento Uchida, Shinichi ShirakawaLarge Language Model-Based Feature Engineering Automated Feature Engineering
Yokohama National University
Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection
Aug 17, 2026Hai Xia, Carlos Ansótegui, Stefan SzeiderAlgorithm Selection Automated Feature Engineering
Algorithms and Complexity Group, TU Wien, Vienna, Austria · Logic & Optimization Group, University of Lleida, Lleida, Spain
Tabular Numeric Stretch Transformation
Aug 10, 2026Zihao Ye, Juyong Kim, Johnna Sundberg +2Tabular ML Automated Feature Engineering
Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213, USA
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Aug 6, 2026Soorya Ram Shimgekar, Michelle Hu, Dorisa Shehi +10Multi-Agent Systems Electronic Health Records
Nimblemind · Singapore University of Technology and Design · University of Illinois Urbana-Champaign +4
When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters
Aug 5, 2026Fangxin Wang, Ziyi Zhang, Diyi Zhuang +4Residual Learning Time Series Forecasting
University of Illinois Chicago · Texas A&M University · Massachusetts Institute of Technology +1
Statistical comparisons of time-series feature sets on classification tasks
Aug 3, 2026Trent Henderson, Ben D. FulcherTime Series Classification Classification
Dynamics and Neural Systems Lab, The University of Sydney, Camperdown, 2006, NSW, Australia.
FunL2O: LLM-Guided Feature Function Design for Learning to Optimize
Jul 29, 2026Bingheng Li, Junyang Cai, Yupeng Zhang +3Large Language Model-Guided Optimization Learning to Optimize
Michigan State University · University of Southern California · University of Wisconsin–Madison +1
Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs
Jul 25, 2026Sha Li, Naren RamakrishnanEvolutionary Optimization Large Language Model-Guided Optimization
Department of Computer Science Virginia Tech Blacksburg, VA 24060 · Department of Computer Science Virginia Tech Alexandria, VA 22305
Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling
Jul 20, 2026Saleh Valizadeh Sotubadi, Nazanin Mahjourian, Vinh NguyenFeature Selection Automated Machine Learning
Department of Mechanical and Aerospace Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, 49931, MI, USA.
EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
Jul 6, 2026Sonali Santhosh, Kelly Shuhong Yu, Eugene Chang +3Electroencephalography LLM-Based Program Synthesis
Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA · Dept. of Neuroscience, University of California, Berkeley, Berkeley, CA, USA · Dept. of Neurology and Neurologic Sciences, Stanford University, Palo Alto, CA, USA +1
Evolutionary Feature Engineering for Structured Data
Jul 2, 2026Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz +4Large Language Model-Guided Optimization Time Series Forecasting
University of Michigan · Google Research
Interpretable vs Learned Encoders for High-Cardinality Fraud Detection
Jul 1, 2026Xiao Han, Jingjing Liu, Moxuan Zheng +2Financial Fraud Detection Tabular ML
Goizueta Business School Emory University Atlanta, GA, USA · Electrical Engineering and Computer Sciences University of California, Berkeley Kirkland, WA · Stern School of Business New York University New York, NY, USA +2
TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging
Jun 29, 2026Guangyu Meng, Pengfei Gu, Xueyang Li +3Medical Image Analysis Persistent Homology
Dept. of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA · Dept. of Computer Science, The University of Texas Rio Grande Valley, Edinburg, TX, USA
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Jun 16, 2026Ruman Wang, Hangting YeMedical Diagnosis Medical Image Classification
Liaoning University of Traditional Chinese Medicine · School of Artificial Intelligence, Jilin University
Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
Jun 12, 2026Liancheng Gong, Zhiyang Wang, Yiwei Xu +1Automated Feature Engineering
University of Maryland, College Park · New York University

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Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search

Ziming Dai, Dabiao Ma, Ziheng Guo, Jack Dong+1

City University of Hong Kong Hong Kong, China

Automated Feature Engineering Large Language Model-Based Feature Engineering Large Language Model-Guided Optimization Program Synthesis

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Industrial risk-control systems typically rely on structured-data models for efficient prediction, yet substantial valuable information remains embedded in unstructured long text. Extracting this information through manual feature engineering is labor-intensive, while requiring a large language model (LLM) to process every real-time input may not meet practical deployment requirements. To address this challenge, we propose LLM-BlockFE, an LLM-guided offline feature construction framework that converts long text into executable feature programs, thereby avoiding LLM calls during online inference. LLM-BlockFE constructs feature programs by incrementally appending immutable code blocks and evaluates candidate features using a downstream model. To address the tendency of conventional greedy search to become trapped in suboptimal solutions, our method introduces a block-level rollback mechanism based on depth-calibrated credit allocation and advances multiple independent search trajectories in an interleaved manner, reducing redundant exploration by sharing fixed descriptions of each trajectory's exploration direction. After the search, the resulting programs are frozen and deployed to extract structured features for downstream prediction models. Across two public and two private datasets, LLM-BlockFE achieves absolute AUC improvements of 0.0069 to 0.0358 over the strongest baseline on each dataset in the full-dataset comparison. Post-launch monitoring across five deployed financial risk-control applications shows absolute KS improvements of 0.02 to 1.56 percentage points over the existing manually designed strategy.