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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 15,059 · see citing works

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 · see citing works

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 · see citing works

Untitled work

(2011) · Cited by 6,993 · see citing works

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 · see citing works

Support Vector Regression Machines

Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,654 · see citing works

An analysis of single-layer networks in unsupervised feature learning

Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,760 · see citing works

Estimating Optimal Transformations for Multiple Regression and Correlation

Breiman, L., & Friedman, J. H. (1985) · Journal of the American Statistical Association · doi:10.1080/01621459.1985.10478157 · Cited by 1,713 · see citing works

Feature Engineering for Text Classification

Scott, S., & Matwin, S. (1999) · Cited by 307 · see citing works

Automatic neural network modeling for univariate time series

Balkin, S. D., & Ord, J. K. (2000) · International Journal of Forecasting · doi:10.1016/s0169-2070(00)00072-8 · Cited by 185 · see citing works

Boosting Algorithms as Gradient Descent in Function Space

Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. (1999) · Cited by 151 · see citing works

ON THE DISTANCE BETWEEN ROOTS OF INTEGER POLYNOMIALS

Bugeaud, Y., & Mignotte, M. (2004) · Proceedings of the Edinburgh Mathematical Society · doi:10.1017/s0013091503000257 · Cited by 52 · see citing works

Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

Punmiya, R., & Choe, S. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2892595 · Cited by 413 · see citing works

Approximate model predictive building control via machine learning

Drgoňa, J., Picard, D., Kvasnica, M., & Helsen, L. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.156 · Cited by 260 · see citing works

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Ashmore, R., Calinescu, R., & Paterson, C. (2022) · ACM Computing Surveys · doi:10.1145/3453444 · Cited by 256 · see citing works

Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

Rupapara, V., Rustam, F., Shahzad, H. F., Mehmood, A., Ashraf, I., & Choi, G. S. (2021) · IEEE Access · doi:10.1109/access.2021.3083638 · Cited by 202 · see citing works

Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review

Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180 · see citing works

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec, J., Börlin, C. S., Buric, F., Muhammad, A. S., Chen, R., Siewers, V., Verendel, V., Nielsen, J., Töpel, M., & Zelezniak, A. (2020) · Nature Communications · doi:10.1038/s41467-020-19921-4 · Cited by 174 · see citing works

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec, J., Buric, F., Muhammad, A. S., Chen, R., Verendel, V., Töpel, M., & Zelezniak, A. (2019) · doi:10.1101/792531 · Cited by 173 · see citing works

Machine learning accelerates the materials discovery

Fang, J., Xie, M., He, X., Zhang, J., Hu, J., Chen, Y., Yang, Y., & Jin, Q. (2022) · Materials Today Communications · doi:10.1016/j.mtcomm.2022.104900 · Cited by 143 · see citing works

A deep learning based stock trading model with 2-D CNN trend detection

Gudelek, M. U., Bölük, S. A., & Özbayoğlu, A. M. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285188 · Cited by 134 · see citing works

A review of machine learning for modeling air quality: Overlooked but important issues

Tang, D., Zhan, Y., & Yang, F. (2024) · Atmospheric Research · doi:10.1016/j.atmosres.2024.107261 · Cited by 141 · see citing works

A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering

Li, Y., Zeng, H., Zhang, M., Wu, B., Zhao, Y., Yao, X., Cheng, T., Qin, X., & Wu, F. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103269 · Cited by 127 · see citing works

High‐temperature ablation resistance prediction of ceramic coatings using machine learning

Sun, J., Zhang, Z., Zhang, Y., Zhang, X., Guo, J., Fu, Q., & Wu, L. (2024) · Journal of the American Ceramic Society · doi:10.1111/jace.20136 · Cited by 104 · see citing works

A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM

Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., & Zhang, Y. (2019) · Sensors · doi:10.3390/s19040947 · Cited by 92 · see citing works

Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study

Ali, M. L., Thakur, K., Schmeelk, S., DeBello, J. E., & Dragos, D. M. (2025) · Applied Sciences · doi:10.3390/app15041903 · Cited by 88 · see citing works

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and ChallengesPreprint

Ashmore, R., Călinescu, R., & Paterson, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04223 · Cited by 86 · see citing works

Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh

Alam, M. S., Al–Ismail, F. S., Hossain, M. S., & Rahman, S. M. (2023) · Processes · doi:10.3390/pr11030908 · Cited by 79 · see citing works

Classification of Shopify App User Reviews Using Novel Multi Text Features

Rustam, F., Mehmood, A., Ahmad, M. I., Ullah, S., Khan, D. M., & Choi, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.2972632 · Cited by 80 · see citing works

Detection of Fake Job Postings by Utilizing Machine Learning and Natural Language Processing Approaches

Amaar, A., Aljedaani, W., Rustam, F., Ullah, S., Rupapara, V., & Ludi, S. A. (2022) · Neural Processing Letters · doi:10.1007/s11063-021-10727-z · Cited by 82 · see citing works

Data Science Approaches for Electrochemical Engineers: An Introduction through Surrogate Model Development for Lithium-Ion Batteries

Dawson-Elli, N., Lee, S. B., Pathak, M., Mitra, K., & Subramanian, V. R. (2018) · Journal of The Electrochemical Society · doi:10.1149/2.1391714jes · Cited by 75 · see citing works

Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition

Hur, T., Bang, J., Huynh‐The, T., Lee, J., Kim, J., & Lee, S. (2018) · Sensors · doi:10.3390/s18113910 · Cited by 74 · see citing works

Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

Al-Najjar, H. A. H., Pradhan, B., Kalantar, B., Sameen, M. I., Santosh, M., & Alamri, A. M. (2021) · Remote Sensing · doi:10.3390/rs13163281 · Cited by 72 · see citing works

Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review

Wang, Z., Xia, L., Yuan, H., Srinivasan, R. S., & Song, X. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105028 · Cited by 69 · see citing works

Ultra-short-term power forecast method for the wind farm based on feature selection and temporal convolution network

Zha, W., Liu, J., Li, Y., & Liang, Y. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.01.024 · Cited by 66 · see citing works

A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Gaye, B., Zhang, D., & Wulamu, A. (2021) · Information · doi:10.3390/info12090374 · Cited by 64 · see citing works

House Prices Prediction with Machine Learning Algorithms

Fan, C., Cui, Z., & Zhong, X. (2018) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3195106.3195133 · Cited by 59 · see citing works

Forecasting and mitigation of global environmental carbon dioxide emission using machine learning techniques

Bhatt, H., Davawala, M., Joshi, T., Shah, M., & Unnarkat, A. P. (2023) · Cleaner Chemical Engineering · doi:10.1016/j.clce.2023.100095 · Cited by 58 · see citing works

Pairing conceptual modeling with machine learning

Maass, W., & Storey, V. C. (2021) · Data & Knowledge Engineering · doi:10.1016/j.datak.2021.101909 · Cited by 55 · see citing works

Comprehensive machine and deep learning analysis of sensor-based human activity recognition

Balaha, H. M., & Hassan, A. E.-S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08374-7 · Cited by 52 · see citing works

Predicting the compressive strength of high-performance concrete using an interpretable machine learning model

Zhang, Y., Ren, W., Chen, Y., Mi, Y., Lei, J., & Sun, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-79502-z · Cited by 66 · see citing works

A Novel Approach for Best Parameters Selection and Feature Engineering to Analyze and Detect Diabetes: Machine Learning Insights

Ali, M. S., Islam, M. K., Das, A. A., Duranta, D. U. S., Haque, M. F., & Rahman, M. H. (2023) · BioMed Research International · doi:10.1155/2023/8583210 · Cited by 49 · see citing works

Human vs. supervised machine learning: Who learns patterns faster?

Kühl, N., Goutier, M., Baier, L., Wolff, C., & Martin, D. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.09.002 · Cited by 54 · see citing works

Artificial Intelligence-Based Medical Data Mining

Zia, A., Aziz, M., Popa, I., Khan, S. A., Hamedani, A. F., & Asif, A. R. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091359 · Cited by 49 · see citing works

Energy Theft in Smart Grids: A Survey on Data-Driven Attack Strategies and Detection Methods

Althobaiti, A., Jindal, A., Marnerides, A. K., & Roedig, U. (2021) · IEEE Access · doi:10.1109/access.2021.3131220 · Cited by 47 · see citing works

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data

Price, G. D., Heinz, M. V., Song, S. H., Nemesure, M. D., & Jacobson, N. C. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02669-y · Cited by 44 · see citing works

Dynamic Event Detection Using a Distributed Feature Selection Based Machine Learning Approach in a Self-Healing Microgrid

Karim, M. A., Currie, J., & Lie, T. T. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2812768 · Cited by 44 · see citing works

A Time-Frequency Based Suspicious Activity Detection for Anti-Money Laundering

Ketenci, U. G., Kurt, T., Önal, S., Erbil, C., Aktürkoğlu, S., & İlhan, H. Ş. (2021) · IEEE Access · doi:10.1109/access.2021.3072114 · Cited by 42 · see citing works

Learning Sentiment Analysis for Accessibility User Reviews

Aljedaani, W., Rustam, F., Ludi, S. A., Ouni, A., & Mkaouer, M. W. (2021) · IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) · doi:10.1109/asew52652.2021.00053 · Cited by 42 · see citing works

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Hadwan, M., Al-Sarem, M., Saeed, F., & Al-Hagery, M. A. (2022) · Applied Sciences · doi:10.3390/app12115547 · Cited by 40 · see citing works

Deep Learning for Image-Based Plant Growth Monitoring: A Review

Tong, Y.-S., Lee, T.-H., & Yen, K. S. (2022) · International Journal of Engineering and Technology Innovation · doi:10.46604/ijeti.2022.8865 · Cited by 39 · see citing works

A novel approach to detect electricity theft based on conv-attentional Transformer Neural Network

Shi, J., Gao, Y., Gu, D., Li, Y., & Chen, K. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108642 · Cited by 38 · see citing works

Optimization of vascular structure of self-healing concrete using deep neural network (DNN)

Wan, Z., Chang, Z., Xu, Y., & Šavija, B. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.129955 · Cited by 37 · see citing works

The art of feature engineering: essentials for machine learning

Duboue, P. A. (2020) · CERN Document Server (European Organization for Nuclear Research) · Cited by 37 · see citing works

Optimal structural characteristics of osteoinductivity in bioceramics derived from a novel high-throughput screening plus machine learning approach

Liu, Y., Cao, Q., Yong, S., Wang, J., Chen, X., Yumei, X., Lin, J., Yang, M., Wang, K., Li, X., et al. (2025) · Biomaterials · doi:10.1016/j.biomaterials.2025.123348 · Cited by 36 · see citing works

Machine learning promotes the development of all-solid-state batteries

Qiu, Y. L., Zhang, X., Tian, Y., & Zhou, Z. P. (2023) · Chinese Journal of Structural Chemistry · doi:10.1016/j.cjsc.2023.100118 · Cited by 35 · see citing works

Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting

Cordeiro-Costas, M., Villanueva, D., Eguía, P., & Granada, E. (2022) · Applied Sciences · doi:10.3390/app12178769 · Cited by 34 · see citing works

Emotion detection from ECG signals with different learning algorithms and automated feature engineering

Oğuz, F. E., Alkan, A., & Schöler, T. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02606-y · Cited by 33 · see citing works

Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations

Klenam, D. E. P., Asumadu, T. K., Vandadi, M., Rahbar, N., McBagonluri, F., & Soboyejo, W. O. (2023) · Results in Materials · doi:10.1016/j.rinma.2023.100455 · Cited by 33 · see citing works

FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

Chatzimparmpas, A., Martins, R. M., Kucher, K., & Kerren, A. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3141040 · Cited by 31 · see citing works

Predicting student performance in interactive online question pools using mouse interaction features

Wei, H., Li, H., Xia, M., Wang, Y., & Qu, H.-M. (2020) · International Conference on Learning Analytics and Knowledge · doi:10.1145/3375462.3375521 · Cited by 35 · see citing works

Machine-learning forecasting of successful ICOs

Meoli, M., & Silvio Vismara, A. (2022) · Journal of Economics and Business · doi:10.1016/j.jeconbus.2022.106071 · Cited by 30 · see citing works

Automated Extraction of Annual Erosion Rates for Arctic Permafrost Coasts Using Sentinel-1, Deep Learning, and Change Vector Analysis

Philipp, M., Dietz, A. J., Ullmann, T., & Kuenzer, C. (2022) · Remote Sensing · doi:10.3390/rs14153656 · Cited by 30 · see citing works

Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools

Lotfi, F., Beheshti, A., Farhood, H., Pooshideh, M., Jamzad, M., & Hamid Beigy, A. (2023) · Algorithms · doi:10.3390/a16030135 · Cited by 29 · see citing works

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin, D., & Lin, B. (2021) · Energy Materials · doi:10.20517/energymater.2021.10 · Cited by 27 · see citing works

Efficient Large Scale NLP Feature Engineering with Apache Spark

Esmaeilzadeh, A., Heidari, M., Abdolazimi, R., Hajibabaee, P., & Malekzadeh, M. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720765 · Cited by 25 · see citing works

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

Çıtmacı, B., Luo, J., Jang, J., Canuso, V., Richard, D., Ren, Y. M., Morales‐Guio, C. G., & Christofides, P. D. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2022.06.044 · Cited by 25 · see citing works

HybridGCN for protein solubility prediction with adaptive weighting of multiple features

Chen, L., Wu, R., Zhou, F., Zhang, H., & Liu, J. K. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00788-8 · Cited by 25 · see citing works

Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Russo, S., & Bonassi, S. (2022) · Nutrients · doi:10.3390/nu14091705 · Cited by 24 · see citing works

Exploring Feature Engineering Strategies for Improving Predictive Models in Data Science

Katya, E. (2023) · Research Journal of Computer Systems and Engineering · doi:10.52710/rjcse.88 · Cited by 23 · see citing works

Intelligent Feature Engineering for Cybersecurity

Maxwell, P., Alhajjar, E., & Bastian, N. D. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006122 · Cited by 23 · see citing works

Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach

Hussain, S., Mustafa, M. W., Al‐Shqeerat, K. H. A., Al‐rimy, B. A. S., & Saeed, F. (2022) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12386 · Cited by 23 · see citing works

Accurate Identification of ADHD among Adults Using Real-Time Activity Data

Kaur, A., & Kahlon, K. S. (2022) · Brain Sciences · doi:10.3390/brainsci12070831 · Cited by 23 · see citing works

Predictive interpretable analytics models for forecasting healthcare costs using open healthcare data

Rao, A. R., Jain, R., Singh, M., & Garg, R. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100351 · Cited by 25 · see citing works

Robust Trajectory Prediction Using Random Forest Methodology Application to UAS-S4 Ehécatl

Hashemi, S. M., Botez, R. M., & Ghazi, G. (2024) · Aerospace · doi:10.3390/aerospace11010049 · Cited by 22 · see citing works

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

Wang, D., & Zhou, Y. (2024) · Financial Innovation · doi:10.1186/s40854-023-00574-3 · Cited by 22 · see citing works

Field-scale UAV-based multispectral phenomics: Leveraging machine learning, explainable AI, and hybrid feature engineering for enhancements in potato phenotyping

Lapajne, J., Vončina, A., Vojnović, A., Donša, D., Dolničar, P., & Žibrat, U. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109746 · Cited by 21 · see citing works

Predicting hatchability of layer breeders and identifying effects of animal related and environmental factors

Bouba, I., Visser, B., Kemp, B., Rodenburg, T. B., & van den Brand, H. (2021) · Poultry Science · doi:10.1016/j.psj.2021.101394 · Cited by 21 · see citing works

Dengue Prediction Using Deep Learning With Long Short-Term Memory

Saleh, A. Y., & Baiwei, L. (2021) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta52612.2021.9515734 · Cited by 21 · see citing works

Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II

Narendra, A., Gibson, S. J., Dainotti, M. G., Bogdan, M., Pollo, A., Liodakis, I., Poliszczuk, A., & Rinaldi, E. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac545a · Cited by 21 · see citing works

Machine Learning for Spacecraft Operations Support - The Mars Express Power Challenge

Lucas, L., & Boumghar, R. (2017) · IEEE International Conference on Space Mission Challenges for Information Technology · doi:10.1109/smc-it.2017.21 · Cited by 21 · see citing works

Machine Learning based System Identification Tool for data-based Energy and Resource Modeling and Simulation

Weber, T., Sossenheimer, J., Schäfer, S., Ott, M., Walther, J., & Abele, E. (2019) · Procedia CIRP · doi:10.1016/j.procir.2018.12.021 · Cited by 21 · see citing works

Automated Feature Engineering for Deep Neural Networks with Genetic Programming

Heaton, J. (2017) · NSUWorks (Nova Southeastern University) · doi:10.13140/rg.2.2.22508.16002 · Cited by 21 · see citing works

Artificial Intelligence in Pathology: A Simple and Practical Guide

Yao, K., Singh, A., Sridhar, K., Blau, J., & Ohgami, R. S. (2020) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000277 · Cited by 20 · see citing works

Maldistribution and dynamic liquid holdup quantification of quadrilobe catalyst in a trickle bed reactor using gamma-ray computed tomography: Pseudo-3D modelling and empirical modelling using deep neural network

Qi, B., Farid, O., Uribe, S., & Al‐Dahhan, M. H. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2020.09.024 · Cited by 20 · see citing works

RAIDER: Reinforcement-Aided Spear Phishing Detector

Evans, K., Abuadbba, A., Wu, T., Moore, K., Ahmed, M., Pogrebna, G., Nepal‬, ‪., & Johnstone, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23020-2_2 · Cited by 20 · see citing works

A data-driven algorithm to detect false data injections targeting both frequency regulation and market operation in power systems

Roy, S. D., Debbarma, S., & Guerrero, J. M. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108409 · Cited by 20 · see citing works

Symbolic regression as a feature engineering method for machine and deep learning regression tasks

Shmuel, A., Glickman, O., & Lazebnik, T. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad513a · Cited by 20 · see citing works

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Aljedaani, W., Abuhaimed, I. M., Rustam, F., Mkaouer, M. W., Ouni, A., & Jenhani, I. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00946-0 · Cited by 19 · see citing works

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

Aljedaani, W., Saad, E., Rustam, F., Díez, I. D. L. T., & Ashraf, I. (2022) · Mathematics · doi:10.3390/math10173199 · Cited by 19 · see citing works

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Kang, D., Ma, J., & Zhao, Y. (2022) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c03307 · Cited by 18 · see citing works

Performance evaluation of optimized and adaptive neuro fuzzy inference system for predictive modeling in agriculture

Remya, S., & Sasikala, R. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106718 · Cited by 18 · see citing works

A dynamic scenario‐driven technique for stock price prediction and trading

Thesia, Y., Oza, V., & Thakkar, P. (2021) · Journal of Forecasting · doi:10.1002/for.2848 · Cited by 17 · see citing works

Prediction of maximum slug length considering impact of well trajectories in British Columbia shale gas fields using machine learning

Kim, S., Yun, Y., Choi, J., Bizhani, M., Kim, T.-W., & Jeong, H. (2022) · Journal of Natural Gas Science and Engineering · doi:10.1016/j.jngse.2022.104725 · Cited by 17 · see citing works

Data Preprocessing

Morr, C. E., Jammal, M., Ali‐Hassan, H., & El-Hallak, W. (2022) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-16990-8_4 · Cited by 17 · see citing works

Deep learning-based approaches for myocardial infarction detection: A comprehensive review recent advances and emerging challenges

Elshafey, R., Hamila, R., & Bensaali, F. (2024) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2024.100322 · Cited by 17 · see citing works

ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

Jia, P., Wang, Y., Du, Z., Zhao, X., Wang, Y., Chen, B., Wang, W., Guo, H., & Tang, R. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671571 · Cited by 17 · see citing works

Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis

de Vargas, D. L., Oliva, J. T., Teixeira, M., Casanova, D., & Rosa, J. L. G. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08350-1 · Cited by 16 · see citing works

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques

Ahmed, S., Khan, D. M., Sadiq, S., Umer, M., Shahzad, F., Mahmood, K., Mohsen, H., & Ashraf, I. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1190 · Cited by 15 · see citing works

Learning Web Content Extraction with DOM Features

Utiu, N., & Ionescu, V.-S. (2018) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2018.8516632 · Cited by 15 · see citing works

Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science

Xiang, Z., Fan, M., Tovar, G. V., Trehern, W., Yoon, B.-J., Qian, X., Arróyave, R., & Qian, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17247 · Cited by 15 · see citing works

Feature guided training and rotational standardization for the morphological classification of radio galaxies

Brand, K., Grobler, T., Kleynhans, W., Vaccari, M., Prescott, M., & Becker, B. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad989 · Cited by 15 · see citing works

Bridging accuracy and interpretability: A rescaled cluster-then-predict approach for enhanced credit scoring

Teng, H.-W., Kang, M.-H., Lee, I.-H., & Bai, L.-C. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.103005 · Cited by 15 · see citing works

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large‐scale analysis

Yanagawa, R., Iwadoh, K., Akabane, M., Imaoka, Y., Bozhilov, K. K., Melcher, M. L., & Sasaki, K. (2024) · Clinical Transplantation · doi:10.1111/ctr.15316 · Cited by 14 · see citing works

Harnessing feature engineering to improve machine learning: A review of different data processing techniques

Abdelouahed, S. M., Rahmouni, A., Asmae, E., & Abdellah, A. (2024) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/iscv60512.2024.10620105 · Cited by 17 · see citing works

Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning

Appeltans, S., Pieters, J., & Mouazen, A. M. (2021) · Remote Sensing · doi:10.3390/rs13071341 · Cited by 14 · see citing works

Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome

Vänni, P., Tejesvi, M. V., Ainonen, S., Renko, M., Korpela, K., Salo, J., Paalanne, N., & Tapiainen, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-97007-x · Cited by 13 · see citing works

A data-driven method for flight time estimation based on air traffic pattern identification and prediction

Yang, C., Zhang, J., Gui, X., Peng, Z., & Wang, B. (2022) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2022.2130693 · Cited by 13 · see citing works

A novel feature engineering method for severity prediction of marine accidents

Li, T., Wang, X., Zhang, Z., & Feng, Y. (2025) · Journal of Marine Engineering & Technology · doi:10.1080/20464177.2025.2469336 · Cited by 13 · see citing works

Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps

Naik, P., Dalponte, M., & Bruzzone, L. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3179027 · Cited by 13 · see citing works

Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense

Mani, G., Haliem, M., Bhargava, B., Manickam, I., Kochpatcharin, K., Kim, M., Vugrin, E. D., Wang, W., Jenkins, C., Angın, P., et al. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3234561 · Cited by 13 · see citing works

Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons

Rodríguez-Collado, A., & Rueda, C. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.684950 · Cited by 13 · see citing works

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