Feature Engineering

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Automated feature engineering, AutoML, and decision-focused learning for improved energy consumption forecasting
Sep 28, 2026Nasser AlkhulaifiElectricity Price Forecasting Energy Resources
School of Computer Science, Faculty of Science University of Nottingham
Explanations-Driven Active Feature Acquisition for Algorithmic Recourse
Sep 14, 2026Vinura Galwaduge, Jagath SamarabanduFeature Engineering Algorithmic Cores
Dept. of Electrical and Computer Engineering Western University London, ON, Canada
Scaling E-Commerce Attribute Extraction with Parallel Decoding
Sep 9, 2026Nikhita Vedula, Dushyanta Dhyani, Bryan Wang +1Information Extraction Catalogs
Amazon.com, Inc.
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Aug 6, 2026Soorya Ram Shimgekar, Michelle Hu, Dorisa Shehi +10Feature Engineering 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 +4Feature Engineering
University of Illinois Chicago · Texas A&M University · Massachusetts Institute of Technology +1
BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition
Aug 3, 2026Jiaorong Feng, Qian Li, Ying LiPool-Based Active Learning Feature Engineering
Curtin Business School, Curtin University, Perth, Western Australia, Australia · †Present affiliation: Independent Researcher. · School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Western Australia, Australia
ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
Jul 30, 2026Ruman Wang, Hangting YeMedical Image Classification Feature Engineering
Liaoning University of Traditional Chinese Medicine · School of Artificial Intelligence, Jilin University
FILLER: Feature Imputation via Latent Location Exploration and Retrieval
Jul 25, 2026Santu Mondal, Chayan Maitra, Rajat K. DeImputation Inpainting
Machine Intelligence Unit Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata 700108
Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs
Jul 25, 2026Sha Li, Naren RamakrishnanFeature Engineering Tabular Learning
Department of Computer Science Virginia Tech Blacksburg, VA 24060 · Department of Computer Science Virginia Tech Alexandria, VA 22305
Format-Controlled Multi-Scale JPEG Compression Response Analysis for Image-Level Forgery Screening
Jul 7, 2026Sujith K MandalaFace Forgery Detection Learned Image Compression
Commonwealth Bank of Australia Bangalore, India
From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn's "The Lark" Integrating Electroacoustic Analysis
Jul 7, 2026Yakun Liu, Zhiyu Jin, Hai Luan +2Generative Music Acoustic Latent Space
Department of Composition, Shenyang Conservatory of Music, Shenyang 110818, Liaoning, China · Department of Musicology, Shenyang Conservatory of Music, Shenyang 110818, Liaoning, China · Education Information Center, Shenyang Conservatory of Music, Shenyang 110818, Liaoning, China
Evolutionary Feature Engineering for Structured Data
Jul 2, 2026Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz +4Feature Engineering Evolutionary Optimization Methods
University of Michigan · Google Research
Interpretable vs Learned Encoders for High-Cardinality Fraud Detection
Jul 1, 2026Xiao Han, Jingjing Liu, Moxuan Zheng +2Fraud Detection Xgboost Model
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
Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition
Jun 30, 2026Xiaochuan Guo, Jihao Gu, Haixu Liu +6Emotion Recognition Few-Shot Learning
Hefei University of Technology, China · University College London, United Kingdom · The University of Sydney, Australia +5
Zero-Shot Active Feature Acquisition via LLM-Elicitation
Jun 17, 2026Binyamin Perets, Natalie Mendelson, Shiran Vainberg +3Pool-Based Active Learning Feature Engineering
Faculty of Medicine, Technion. · NVIDIA
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Jun 16, 2026Ruman Wang, Hangting YeMedical Image Classification Feature Engineering
Liaoning University of Traditional Chinese Medicine · School of Artificial Intelligence, Jilin University
REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation
Jun 10, 2026Simbarashe Aldrin Ngorima, Albert Helberg, Marelie H. DavelChannel Estimation Channel
Faculty of Engineering, North-West University, South Africa · Centre for Artificial Intelligence Research, South Africa · National Institute for Theoretical and Computational Sciences, South Africa
LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
Jun 8, 2026Ankai Hao, Ke Chen, Huan Li +1Feature Engineering Taco
Zhejiang University
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
Jun 7, 2026Varun Khurana, Vijval Ekbote, Vashu Chauhan +3Feature Engineering Expert Demonstrations
Adobe Media and Data Science Research · IIIT-Delhi
A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction
Jun 4, 2026June-Woo Kim, Kangwook Jang, Minu Kim +1Vocal Tract Shape High-Order Correlations
Department of Electronic Engineering, Wonkwang University, Republic of Korea · AI Convergence Research Institute, Wonkwang University, Republic of Korea · GIST InnoCORE AI-Nano Convergence Institute for Early Detection of Neurodegenerative Diseases, Gwangju Institute of Science and Technology, Republic of Korea +2
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Jun 1, 2026Andrej Tschalzev, Nick Erickson, Yuyang Wang +4Tabular Learning 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 +3Feature Engineering Ai-Based
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
Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning
May 9, 2026Emmanuel AdutwumSupport Vector Machines Feature Engineering
Soka University of America, Aliso Viejo, California, United States.
Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization
May 8, 2026Leonel Aguilar, Jan Nagler, Christoph Hoelscher +1Out-Of-Distribution Extrapolation
Chair of Cognitive Science, ETH Zürich · Centre for Human and Machine Intelligence, Frankfurt School · Aisot Technologies AG +1
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
May 6, 2026Linus Aronsson, Morteza Haghir ChehreghaniAcquisition Feature Engineering
Department of Computer Science and Engineering · Chalmers University of Technology & University of Gothenburg · Gothenburg, Sweden
SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering
Apr 29, 2026Minhee Park, Seongyeon Son, Yonghyun Lee +1Feature Engineering Tabular Learning
FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
Apr 24, 2026Hojjat Karami, David Atienza, Jean-Philippe Thiran +1Electronic Health Records Feature Engineering
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 Reasoning Strategies Model Discovery
University of Oxford · Vela Research · Amazon
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
Feb 10, 2026Zizheng Zhang, Yiming Li, Justin Xu +6Feature Engineering Tabular Learning
Microsoft Research · University of Oxford
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Mar 18, 2024Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed +4Cardiac Electronic Health Records
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA

Sep 28, 2026, cs.AIJ/K move · Enter open · S save

Automated feature engineering, AutoML, and decision-focused learning for improved energy consumption forecasting

Nasser Alkhulaifi

School of Computer Science, Faculty of Science University of Nottingham

Electricity Price Forecasting Energy Resources Feature Engineering

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The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (ECF) supports planning by predicting future consumption, but Machine Learning (ML) models for ECF often depend on expert-driven Feature Engineering (FE). This thesis addresses that dependence through three contributions. First, it establishes and evaluates a comprehensive FE pipeline for ECF and investigates domain-specific features. Second, it introduces AutoEnergy, a domain-tailored automated FE algorithm that generates interpretable features from timestamps and lagged consumption and integrates with AutoML for end-to-end ECF modelling. Across eighteen real-world energy datasets spanning residential, commercial, industrial, renewable, and grid domains, AutoEnergy reduces forecasting error by 19.52%-84.72% relative to baseline AutoML and established automated FE methods, while running 1.31-4.41 times faster, with gains varying by dataset. Third, AutoEnergy is integrated with Decision-Focused Learning (DFL) for a Battery Energy Storage System problem, jointly forecasting electricity prices and demand while optimising charging and discharging decisions. On a real-world UK property dataset, this approach reduces operating costs by 22.9%-56.5% compared with the same DFL models without automated FE. Overall, the results show that domain-specific automated FE can reduce reliance on manual feature design, improve forecasting accuracy, and translate predictive gains into measurable operational benefits in energy management.

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