An empirical analysis of feature engineering for predictive modeling

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

An Analysis of Transformations

Box, G. E. P., & Cox, D. R. (1964) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1964.tb00553.x · Cited by 15,356

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

A training algorithm for optimal margin classifiers

Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992

Untitled work

(2011) · Cited by 6,993

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694

Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid

Hoffmann, V., Torsæter, B. N., Rosenlund, G. H., & Andresen, C. (2022) · Algorithms · doi:10.3390/a15060188 · Cited by 3

Suspicious Activity Monitoring System using Machine Learning

Raja, D. P., Sheelambigai, P., Sruthi Meera, P., & Valarmathi, K. (2023) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces57224.2023.10192786 · Cited by 2

FRAME-LEVEL PROXIMITY AND TOUCH RECOGNITION USING CAPACITIVE SENSING AND SEMI-SUPERVISED SEQUENTIAL MODELING

Fischer, T., Etchart, M., & Biempica, E. (2018) · Machine learning for signal processing ... · doi:10.1109/mlsp.2018.8516922 · Cited by 2

Identification of High Pressure Critical Links in Water Distribution Systems

Jain, S., Shrivastava, S., Saquib, Z., Shah, S., & Rodrigues, A. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697566 · Cited by 2

Object measurements from 2D microscopy images

Bajcsy, P., Chalfoun, J., Simon, M., Kociołek, M., & Brady, M. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-814972-0.00007-2 · Cited by 2

Machine Learning methods for mood disorder decision support

Oleksy, T. (2017) · NORA - Norwegian Open Research Archives · Cited by 2

Integrating Psychometric Analysis and Machine Learning to Augment Data for Cheating Detection in Large-Scale AssessmentPreprint

Jiao, H., Yadav, C., & Li, G. (2023) · doi:10.31234/osf.io/fjz2c · Cited by 2

Efficient Methods to Improve the Performance of Supervised Learning Models

Kumar, M., Yadav, P. D., Singh, H., & Arora, A. (2021) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit51480.2021.9498387 · Cited by 1

Data Mining Assisted Purchase Prediction

Faseeh, M., & Rehman, Z. U. (2022) · Foundation University Journal of Engineering and Applied Sciences · doi:10.33897/fujeas.v2i2.497 · Cited by 1

Diagnosis of carpal tunnel syndrome using deep learning with comparative guidance

Sim, J., Lee, S., Kim, S., Jeong, S.-H., Yoon, J.-S., & Baek, S. (2025) · Clinical Neurophysiology · doi:10.1016/j.clinph.2025.03.038 · Cited by 1

Optimized Feature Engineering for Dentition based Cattle Age Estimation

Guru, D. S., Swaroop, D., Anusha, P., Keerthana, N., & Shivaprasad, D. L. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.334 · Cited by 1

Prediction of the onset of the RSV epidemic with meteorological data using deep neural networks

Yonekura, K., Nishio, M., Kashiwado, M., Naruto, T., & Mori, M. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101659 · Cited by 1

Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching

Li, J., Ren, G., Pan, Y., Sun, J., Wang, P., Xu, F., & Liu, Z. (2025) · Remote Sensing · doi:10.3390/rs17142390 · Cited by 1

Machine Learning-Based Detection of Forensic Artifacts and Anomalies in Tails OS

Kolog, C., Chi, H., & Yan, J. (2025) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt65785.2025.11193360 · Cited by 1

Analysis of Feature Engineering in Deep Learning Architectures for Dynamic Electricity Price Forecasting

Joshi, P., Bhardwaj, J., Størdal, S., Kolhe, M. L., & Haugom, E. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11165808 · Cited by 1

Operational data management and analysis system based on the IQR-isolation forest algorithm and DeepSeek model for SHINE

wang, R., Chen, G., Chen, J., Lv, H., 苗海锋, & Yan, Y. (2026) · Radiation Detection Technology and Methods · doi:10.1007/s41605-026-00656-7 · Cited by 1

Analyzing how MIS can optimize the distribution of energy in smart grids, focusing on data-driven decision-making processes

Hossain, A., Oyebode, D. F., Imon, K. A. A., Faisal, S. M. W., & Vayyala, R. (2025) · Journal of Smart Internet of Things · doi:10.2478/jsiot-2025-0004 · Cited by 4

Evaluating Simulated User Interaction and Search Behaviour

Zerhoudi, S., Granitzer, M., Seifert, C., & Schloetterer, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99739-7_28 · Cited by 4

Data Lakes Lógicos Como Plataformas Para Dados Governamentais em Sociedades e Cidades Inteligentes

Ramos, G. S., Fernandes, D. B., Coelho, J. A. P. D. M., & Aquino, A. L. L. (2022) · Workshop de Computação Aplicada em Governo Eletrônico (WCGE) · doi:10.5753/wcge.2022.223047 · Cited by 1

AI Based Novel Feature Engineering using ANN Applied on Women SES Classification

Lakshmi, B. R., Savitha, N. J., DHAKE, S. P., Jyothi, N. M., Barri, H. K., & Dash, M. (2023) · International Conference on Networks, Multimedia and Information Technology (NMITCON) · doi:10.1109/nmitcon58196.2023.10275979 · Cited by 1

Intention Reasoning for Unmanned Swarm Based on Cluster-Feature-Enhanced Attention Mechanism

Li, Y., Li, Y., Huang, Q., & Chen, X. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1083-6_34 · Cited by 1

Comparative Study: Mouth Brooding Fish (MBF) as a Novel Approach for Android Malware Detection

Zhou, K., Wang, P., & He, B. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150521 · Cited by 1

Titanic Survival Prediction Based on Machine Learning Algorithms

Zhang, Y. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/mwkr1a24 · Cited by 1

Development of a deep neural network and empirical model for predicting local gas holdup profiles in bubble columns

Uribe, S., Alalou, A., Cordero, M. E., & Al‐Dahhan, M. H. (2024) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25556 · Cited by 2

Hydraulic Fracturing Pressure Prediction with Deep Learning Approach

Yong-gang, X., Qian, M., Zengqiang, J., Ting, J., Yang, S., Yifan, D., Norah, L., Johnson, W., Kevin, C., Li, H., et al. (2025) · SPE Oklahoma City Oil and Gas Symposium (OKOGS) · doi:10.2118/224331-ms · Cited by 1

Predicting Machine Learning Model Performance from Dataset Characteristics

Efe, A. G., Çelikten, T., Ergün, A. E., & Onan, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-97992-7_17 · Cited by 1

Modelo de Detecção de Fraudes Elétricas Baseado em Aprendizado de Máquina

Pfeiff, G., Araújo, F., Oliveira, H. M. N. D. S., do Rosário, D. L., & Cerqueira, E. (2020) · doi:10.5753/sbcup.2020.11211 · Cited by 1

Estimate House Price Using Machine Learning

Gadde, R., Amiruzzaman, M., Burns, R., Islam, M. R., & Nor, R. M. (2023) · Research Briefs on Information and Communication Technology Evolution · doi:10.56801/rebicte.v9i.156 · Cited by 1

Self-Supervised Machine Learning Approach for Identifying Biochemical Influences on Protein-Ligand Binding AffinityPreprint

Singh, A. (2021) · Research Square · doi:10.21203/rs.3.rs-1091733/v1 · Cited by 1

A Novel Approach to Detect Electricity Theft Based on Conv-Attentional TransformerPreprint

Shi, J., Gao, Y., Gu, D., Li, Y., & Chen, K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4069701 · Cited by 1

Computer Science & Information Technology (CS & IT)

(2020) · doi:10.5121/csit.2020.1015 · Cited by 1

Modeling Lengthy Behavioral Log Data for Customer Churn Management: A Representation Learning ApproachPreprint

Ahn, D., Lee, D., & Hosanagar, K. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3969455 · Cited by 1

Evaluation of Geospatial Features for Forecasting Parking Occupancy Using Social Media Data

Riedel, J. (2017) · doi:10.23860/thesis-riedel-johannes-2017

A Dynamically Adaptive Movie Occupancy Forecasting System with Feature Optimization

Sundararaman, V., Ramesh, A., Sharan Sundar, S., Jain, A. K., Gudur, G. K., & Vijayaraghavan, V. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2019.00118 · Cited by 2

Providing Real-Valued Actions for Tangled Program Graphs Under the CartPole Benchmark

Wright, M. A. (2020) · DalSpace (Dalhousie University)

Feature Engineering for Scalable Application-Level Post-Silicon DebuggingPreprint

Pal, D., & Vasudevan, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04554

Parametric design investigation on geometric orifice area and coaptation area of polymeric heart valves

Gülbulak, U. (2020)

Top squark signal significance enhancement by different Machine Learning AlgorithmsPreprint

Jorge, F., Ronald, R., Jesus, S., Juan, M., & Ávila, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06813

Detection of leek rust and white tip disease under field conditions using hyperspectral proximal sensing and supervised machine learning

Appeltans, S., Pieters, J., & Mouazen, A. M. (2021) · Ghent University Academic Bibliography (Ghent University)

Законодательная деятельность Совета Рабочей и Крестьянской обороны в годы Гражданской войны в России (1918-1920 гг. )

Владимирович, С. И. (2012) · Bulletin of Chelyabinsk State University

Using Feature Engineering Techniques to Improve Multivariate Model Forecasting of the South African Unemployment Rate

Makola, T., Mulaudzi, R., & Ajoodha, R. (2021) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698753

LSD: Discrimination of Coal Mining Accident’s Causes Based on Ensemble Machine Learning

Javaid, M. A., Shahroz, M., Mushtaq, M. F., Ali, M. R., Sharif, W., Ashraf, A., & Ghouse, M. G. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-00828-3_39

Context-based Data Augmentation for Improved Ballet Pose Recognition

Bowditch, M., & van der Haar, D. T. (2022) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd54961.2022.9856109

Cri-Astrologer: Predicting Demography of Involved Criminals based on Historical Data

Rahman, M. A., & Islam, A. B. M. A. A. (2022) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3569551.3569561

A robust AI Agent-based approach to tackle and prevent Money LaunderingPreprint

Wang, Q. (2022) · doi:10.31219/osf.io/bd38t

Visual Analytics for Explainable and Trustworthy Machine Learning

Chatzimparmpas, A. (2023) · doi:10.15626/lud.482.2023

HDA-TIP: A Framework for Heterogeneous Data Aggregation for Threat Intelligence Platform

Yasmeen, A., Ullah, K. K., & Muhammad, A. (2023) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom56909.2023.10035604

Feature Engineering for Flow‐Based IDS

Adhao, R. B., & Pachghare, V. (2022) · doi:10.1002/9781119777465.ch5

Peer Review #2 of "Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques (v0.2)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1190v0.2/reviews/2

Peer Review #1 of "Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques (v0.2)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1190v0.2/reviews/1

Peer Review #1 of "Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques (v0.1)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1190v0.1/reviews/1

Peer Review #2 of "Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques (v0.1)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1190v0.1/reviews/2

Model Explainability in Physiological and Healthcare-based Neural NetworksPreprint

Li, B., Sharma, R., & Gupta, A. (2023) · doi:10.22541/au.167907495.58726050/v1

Deep Neural Networks system for Automatic Emotion RecognitionPreprint

Zhu, H., Wang, G., Cambria, J., & Ghosh, A. (2023) · doi:10.36227/techrxiv.22558123.v1

Deep Neural Networks system for Automatic Emotion RecognitionPreprint

Zhu, H., Wang, G., Cambria, J., & Ghosh, A. (2023) · doi:10.36227/techrxiv.22558123

Implementation of Exploratory Data Analysis on Weather Data

Adivarekar, S., Nanwani, S., Mandal, N., & Sarode, T. (2023) · International Conference on Communication System, Computing and IT Applications (CSCITA) · doi:10.1109/cscita55725.2023.10104864

Estimating Remote PPG via Depression Analysis of Facial VideosPreprint

Jianyi, A., Sihag, V., Schindler, C., & Sharma, A. (2023) · doi:10.31237/osf.io/e4b7n

Estimating Remote PPG via Depression Analysis of Facial VideosPreprint

Sihag, V., Bauer, M. L. M., Sharma, A., & Schindler, C. (2023) · doi:10.31237/osf.io/nsxwa

Remote Estimation of Vital Signs Using Dynamic Facial Region Selection for PhotoplethysmographyPreprint

Singh, A., Chander, V., Yi, & Mishra, A. (2023) · doi:10.31219/osf.io/wgxre

Remote PPG vital sign Estimation with adaptive facial regionsPreprint

Traeger, A., Mondal, S., & Jha, A. (2023) · doi:10.31219/osf.io/tzvbx

The Age of the Meta-Doctor: Diagnosing Parkinson’s Disease with Artificial Intelligence and Speech

Tripathi, A., Appavu, R., & Kethar, J. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i2.4379

Estimating vital signs remotely using adaptive facial regions for photoplethysmographyPreprint

VERMA, A., kaur, G., Zapana, B. D. O., Yi, & Reid, S. (2023) · doi:10.31237/osf.io/dz27g

Lognormality: An Open Window on Neuromotor Control

Réjean Plamondon, B., Bensalah, A., Lebel, K., Salameh, R., Broin, G. S. D., O’Reilly, C., Begon, M., Desbiens, O., Beloufa, Y., Guy, A., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45461-5_15

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Aljedaani, W. (2023) · doi:10.12794/metadc2137559

Forecasting Goal-Oriented Activities: A Deep Learning Approach Leveraging Cross-Activity RelationshipsPreprint

Zhu, Y., Bi, X., Adomavičius, G., & Curley, S. P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4651870

A TIME FREQUENCY BASED SUSPICIOUS ACTIVITY DETECTION FOR ANTIMONEY LAUNDERING

Ponaganti, M. (2019) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.61841/turcomat.v10i1.14505

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu, L., & Zhu, Y. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150522

Predictive Modeling with Feature Engineering and Outlier Detection on Airbnb Dataset

Gajul, S., & Potey, M. A. (2024) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit61985.2024.10626884

A Behavioral Recognition Method with Data Fusion and Supervised Learning for Tele-Assistance Systems

Soviany, S., GHEORGHE, C.-G., Paraschiv, E.-A., & Bica, O. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-62523-7_2

CuES: Conditional Uncorrelation-based Characteristic Enhancement and Fusion of Electrical Signals

Wang, Z., Zhang, Y., Zhou, J., Cai, H., Liu, X., Wang, J., & Zheng, N. (2024) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion59988.2024.10706331

Theory-Guided Data-Science for Battery Modeling: Insights from a Comparative Study

Bielski, P., Denner, N., Bischof, S., Rzepka, B., & Böhm, K. (2024) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa61799.2024.10722776

To the analysis of methods and mechanisms of predictive modeling of onboard equipment reliability when solving problems of aircraft maintenance workload planning

Б.И., О., В.Д., Б., Д.О., С., Gorbakon, N. V., & Vlasova, A. V. (2025) · Civil Aviation High TECHNOLOGIES · doi:10.26467/2079-0619-2025-28-2-35-50

Qualitative Analysis in LIBS

Képeš, E. (2025) · Springer eBooks · doi:10.1007/978-3-031-85975-5_4

Method Comparison and Case Analysis in Machine Learning

Dong, Z. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94892-3_4

Recognizing Food Neophobia Through Consumer Behavior in Virtual Reality

Zhao, J., Wang, J., Wallgrün, J. O., de-Vries, R., & Klippel, A. (2025) · International Conference on Virtual Reality · doi:10.1109/icvr66534.2025.11172652

An Information Fusion Model Based on Error–Dependent High–Order Fuzzy Cognitive Maps and Long Short–Term Memory Network for Financial Market Prediction

Zhang, Y., Song, Y., & Wei, G. (2025) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622026500045 · Cited by 1

Prediction the Short-term Exchange Rate of USD/IRR Using Deep Learning and the Impact of Sentiment Analysis Features on it

Farsani, S. J. M., Eshlaghy, A. T., & Radfar, R. (2024) · Journal of Money and Economy · doi:10.61882/jme.19.2.249

A machine learning-based simulation experiment on the impact of transportation energy consumption on carbon emissions in the United Kingdom

Samuel, I., & Adedoyin, F. F. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33971-4.00007-6

An Intersection of Artificial Intelligence and Healthcare: A Focus on Polycystic Ovary Syndrome Diagnosis

Amissah, D. K., Boante, L. M., & Appati, J. K. (2025) · Operations Research Forum · doi:10.1007/s43069-025-00565-3 · Cited by 1

Low- and high-beta stock rotation trading by reinforcement learning policies

Jahangiri, E., & Corazza, M. (2025) · Decisions in Economics and Finance · doi:10.1007/s10203-025-00546-5

G2IFS: Global-to-Instance Feature Selection in Deep Recommender System

Chen, L., You, L., lu, J., Huang, X., & Nie, X. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760802

FIGURES 23–31 in Five new leafhopper species of the genus Typhlocyba Germar (Hemiptera: Cicadellidae: Typhlocybinae) from China

Huang, M., & Zhang, Y.-L. (2009) · INFM-OAR (INFN Catania) · doi:10.5281/zenodo.185064

Additional file 1 of An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression

Wang, D., & Zhou, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26691553

Predicting Customer Behavior in Autonomous Retail Application: A Classification-Based Approach

Rezaei, A., Jahani, A., Nejad, D. A., Mehrabi, A., Khalilpour, S. A., Seyedi, H. K., & Taghirad, H. D. (2024) · RSI International Conference on Robotics and Mechatronics (ICRoM) · doi:10.1109/icrom64545.2024.10903646

Additional file 2 of An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression

Wang, D., & Zhou, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26691556

Machine Learning Methods for SQL Objects in Oracle Database

Kunalic, A., Kapo, M., Batalović, M., & Buza, E. (2025) · International Conference on Communications, Information, Electronic and Energy Systems (CIEES) · doi:10.1109/ciees66347.2025.11300166

Interpretable Fuel Volume Prediction in Fleet Management Using LIME-based Explainable AI and Linear Regression

Purba, P. L., Widiantoro, A. D., & Dewi, S. I. S. (2025) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic68054.2025.11309623

Translational Study of Using FOCM/TS Metabolites for Supporting Autism Spectrum Disorder Diagnosis

Arici, H., Causey, M., Patra, S., Krüger, U., Uribe, C. A. V., Melmed, R. D., Ciuk, C., Crisler, S., Marler, S., Cundiff, A. W., et al. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04365-6

A Comprehensive Study on Olympic Data Analysis for Predicting Performance Trends using Machine Learning Algorithm

Raval, S., Mavani, A., Munde, S., Panchal, R., Wankhade, S., & Sakarkar, G. (2025) · International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) · doi:10.1109/icuis67429.2025.11380487

EHCFE: Enhanced Hierarchical Clustering with Feature Engineering for Automating Labeling of Student Performance and Dropout Prediction

Alghanmi, N. (2026) · Electronics · doi:10.3390/electronics15061265

Qualité de l'information dans les médias sociaux en ligne et collection de big data : un exemple de détection de spam sur twitter

Washha, M. (2018) · theses.fr (ABES) · doi:10.70675/1df31d12zac24z4c4ez844czf51176438798

Intrusion Detection in Cyber-Physical Systems for enhanced Cybersecurity of Water Management Systems

Ukwuoma, H. C. (2025) · doi:10.70675/be2a56f7z2468z4008z99e0z96aaa0248ba9

Prediction and interpretation of NH3 and HCN formation in biomass pyrolysis: A novel machine learning architecture driven by multiple feature engineering techniques

Liu, X., Wang, M., Yang, Y., & Yang, H. (2026) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2026.107837

Predicting and Explaining Customer Data Sharing in the Open Banking

de Brito, J. B. G., Heldt, R., Silveira, C. S., Bogaert, M., Bucco, G. B., Luce, F. B., Becker, J. L., Zabala, F. J., & Anzanello, M. J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-25314-9_8

Method of Estimating Electric Vehicle Charge Time Based on Neural Networks

Xie, Z., Shojaei, S., & Weslati, F. (2026) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2026-01-0382

THC-IDS: temporal Hebbian-enhanced contrastive Learning for IDS

Tong, F., & Zhang, A. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08679-z

An explainable hybrid machine learning approach for automated drug identification for safe dosing in smart healthcare systems

Shukla, M. M., Chaurasia, B. K., Mishra, A., & Pal, T. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01478-8

A Hybrid Deep Learning Approach for Small-Sample TOC Prediction in Saline Lacustrine Shale

Yuan, B., Zhang, B., Zhang, Y., Zhang, J. Y., Hu, T., Qiu, N., Wang, Z., Ma, M., Xiong, Z., Wang, M., et al. (2026) · Energies · doi:10.3390/en19143360

Traffic Speed Prediction Incorporating Temporal Dependency and Spatial Structure Based on Urban Big Data

Lee, S., & Yu, H.-J. (2026) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd69640.2026.11637185

A Data‐Driven Investigation of How Forecast Horizon and Training Data Size Influence Machine Learning Performance in Solar Irradiance Forecasting

Keramati, F., & Mohammadi, H. R. (2026) · Journal of Forecasting · doi:10.1002/for.70208

The Role of Feature Engineering in Enhancing Machine Learning Models

Iqbal, F., & Ali, H. (2023) · doi:10.13140/rg.2.2.16779.26404

Feature Engineering for Machine Learning by Alice Zheng & Amanda Casari Computers

Casari, A. Z. A. (2018)

How Much Feature Engineering Is Too Much? Uncovering Proxy Substitution and the Dose-response Limits of Interaction Synthesis in Feature SelectionPreprint

Utsho, S. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7299400

Feature Engineering in Machine Learning: Techniques, Challenges, and Best Practices - A Comprehensive Review

Katta, M. (2026) · International Journal of Science and Research (IJSR) · doi:10.21275/sr25223105328

A Comparative Model of Feature Engineering With and Without Domain Knowledge

Bohra, R., Karki, P., & Kumudavalli, M. V. (2025) · International Journal of Computer Sciences and Engineering