Automated Feature Engineering

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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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
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
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
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
LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
Jun 8, 2026Ankai Hao, Ke Chen, Huan Li +1LLM Evaluation Tabular ML
Zhejiang University
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
Jun 7, 2026Varun Khurana, Vijval Ekbote, Vashu Chauhan +3Interpretable ML Automated Feature Engineering
Adobe Media and Data Science Research · IIIT-Delhi
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Jun 1, 2026Andrej Tschalzev, Nick Erickson, Yuyang Wang +4Tabular ML Automated Feature Engineering
University of Mannheim · Prior Labs · AWS +3
Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
May 24, 2026Hangxuan Li, Renjun Jia, Xuezhang Wu +3LLM-Based Program Synthesis Cloud Computing
Alibaba Cloud Computing Co. Ltd, Hangzhou, China · School of Computer Science, Fudan University, Shanghai, China · School of Computer Science and Technology, Tongji University, Shanghai, China +1
Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications
May 14, 2026Matteo Cobelli, Stefano SanvitoMaterials Property Prediction Automated Feature Engineering
School of Physics and CRANN Institute, Trinity College, Dublin 2, Dublin, Ireland.
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
May 12, 2026Zhiming Yu, Wangtao Lu, Xin LaiNonlinear System Identification Automated Feature Engineering
School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, China · Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
RelAgent: LLM Agents as Data Scientists for Relational Learning
May 8, 2026Xingyue Huang, Louis Tichelman, Jinwoo Kim +2Interpretable ML LLM Agents
University of Oxford · AITHYRA · TU Wien +1
SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering
Apr 29, 2026Minhee Park, Seongyeon Son, Yonghyun Lee +1Tabular ML Automated Feature Engineering
FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
Apr 24, 2026Hojjat Karami, David Atienza, Jean-Philippe Thiran +1Healthcare Electronic Health Records
1École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Apr 23, 2026Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6LLM Prompting Automated Feature Engineering
University of Oxford · Vela Research · Amazon
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
Apr 22, 2026Fengxian Dong, Zhi Zheng, Xiao Han +5Tabular ML Multi-Agent LLMs
University of Science and Technology of China · Zhejiang University of Technology · Meituan
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
Feb 10, 2026Zizheng Zhang, Yiming Li, Justin Xu +6Interpretable ML Clinical Prediction
Microsoft Research · University of Oxford

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AutoDP-LLM: Automating Data Pre-processing for Intrusion Detection Systems using Large Language Models

Bao-Phong Nguyen, Gia-Khanh Pham, Thai-Duong Do, Mai Xuan Trang+6

Business AI Lab, College of Technology, National Economics University, Hanoi, Vietnam

Network Intrusion Detection LLMs for Cybersecurity Automated Feature Engineering

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The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems (IDS). However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative configurations. For large, high-dimensional network traffic data, this process can create a significant computational burden. In this work, we propose AutoDP-LLM, an automated pre-processing framework designed to reduce manual pipeline development and computational overhead. Specifically, AutoDP-LLM leverages Large Language Models (LLMs) to autonomously generate and validate executable data pre-processing pipelines. The framework combines deterministic host-side planning with LLM-based specialist agents to formulate data-processing strategies, synthesize executable code, and adaptively determine retained feature sets using semantic reasoning and training-derived statistical evidence, without requiring a predefined feature budget. Focusing on multiclass intrusion detection, we evaluate AutoDP-LLM on the UNSW-NB15 and NSL-KDD benchmark datasets using multiple downstream classifiers. Comparative experiments against conventional feature-selection methods show that AutoDP-LLM achieves competitive detection performance while automating the generation of compact and executable pre-processing pipelines. Component-level ablation experiments further demonstrate the complementary contributions of the semantic and statistical feature-reduction components. The repeated generation, validation, execution, and assessment of candidate pipelines are amenable to parallel execution, highlighting the potential of scalable computing environments, including high-performance computing (HPC) systems, to support automated IDS pipeline development.

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Latest papers 31