Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212 · see citing works

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · see citing works

PLS-regression: a basic tool of chemometrics

Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084 · see citing works

Least angle regression

Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357 · see citing works

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works

Sure Independence Screening for Ultrahigh Dimensional Feature Space

Fan, J., & Lv, J. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2008.00674.x · Cited by 2,643 · see citing works

Performance of some variable selection methods when multicollinearity is present

Chong, I.-G., & Jun, C. (2005) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.12.011 · Cited by 2,059 · see citing works

Deep learning for sentiment analysis: A survey

Zhang, L., Wang, S., & Liu, B. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1253 · Cited by 2,058 · see citing works

Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).

Vrieze, S. I. (2012) · Psychological Methods · doi:10.1037/a0027127 · Cited by 1,948 · see citing works

Applied Linear Regression

Weisberg, S. (2005) · Wiley series in probability and statistics · doi:10.1002/0471704091 · Cited by 1,454 · see citing works

Collinearity, Power, and Interpretation of Multiple Regression Analysis

Mason, C. H., & Perreault, W. D. (1991) · Journal of Marketing Research · doi:10.1177/002224379102800302 · Cited by 1,026 · see citing works

Multicollinearity

Alın, A. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.84 · Cited by 993 · see citing works

A new class of blased estimate in linear regression

Kejian, L. (1993) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929308831027 · Cited by 663 · see citing works

CNNpred: CNN-based stock market prediction using a diverse set of variables

Hoseinzade, E., & Haratizadeh, S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.029 · Cited by 645 · see citing works

Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data

Kim, T., & Kim, H. Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212320 · Cited by 347 · see citing works

I. Problems and Designs of Cross-Validation 1

Mosier, C. I. (1951) · Educational and Psychological Measurement · doi:10.1177/001316445101100101 · Cited by 315 · see citing works

Using Liu-Type Estimator to Combat Collinearity

Liu, K. (2003) · Communication in Statistics- Theory and Methods · doi:10.1081/sta-120019959 · Cited by 313 · see citing works

Diagnosing and Dealing with Multicollinearity

Schroeder, M. A., Lander, J., & Levine‐Silverman, S. (1990) · Western Journal of Nursing Research · doi:10.1177/019394599001200204 · Cited by 303 · see citing works

Selection of the Best Subset in Regression Analysis

Hocking, R. R., & Leslie, R. N. (1967) · Technometrics · doi:10.1080/00401706.1967.10490502 · Cited by 299 · see citing works

Selection of Variables for Fitting Equations to Data

Gorman, J. W., & Toman, R. J. (1966) · Technometrics · doi:10.1080/00401706.1966.10490322 · Cited by 282 · see citing works

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

Chen, C., Tsai, Y., Chang, F., & Lin, W. (2020) · Expert Systems · doi:10.1111/exsy.12553 · Cited by 217 · see citing works

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

Kibria, B. M. G., & Lukman, A. F. (2020) · Scientifica · doi:10.1155/2020/9758378 · Cited by 204 · see citing works

Comparison of statistical and machine learning methods in modelling of data with multicollinearity

Garg, A., & Tai, K. (2013) · International Journal of Modelling Identification and Control · doi:10.1504/ijmic.2013.053535 · Cited by 154 · see citing works

Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression

Maitra, S., & Yan, J. (2008) · Cited by 154 · see citing works

Applications of Ridge Analysis Toregression Problems

Horel, A. E. (1962) · Chemical engineering progress · Cited by 150 · see citing works

A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection

Bies, R. R., Muldoon, M. F., Pollock, B. G., Manuck, S. B., Smith, G. S., & Sale, M. E. (2006) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-006-9004-6 · Cited by 132 · see citing works

Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria

Katrutsa, A., & Strijov, V. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.048 · Cited by 131 · see citing works

Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks

Huynh, H. T., & Won, Y. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.07.016 · Cited by 117 · see citing works

A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking

Senawi, A., Wei, H., & Billings, S. A. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.01.026 · Cited by 109 · see citing works

Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor

Tamura, R., Kobayashi, K., Takano, Y., Miyashiro, R., Nakata, K., & Matsui, T. (2018) · Journal of Global Optimization · doi:10.1007/s10898-018-0713-3 · Cited by 102 · see citing works

An explanation of the use of principal-components analysis to detect and correct for multicollinearity

Lafi, S. Q., & Kaneene, J. B. (1992) · Preventive Veterinary Medicine · doi:10.1016/0167-5877(92)90041-d · Cited by 96 · see citing works

On multiple regression analysis

Hamaker, H. C. (1962) · Statistica Neerlandica · doi:10.1111/j.1467-9574.1962.tb01184.x · Cited by 86 · see citing works

Diagnosing and correcting the effects of multicollinearity: Bayesian implications of ridge regression

Assaf, A. G., Tsionas, E. G., & Tasiopoulos, A. E. (2018) · Tourism Management · doi:10.1016/j.tourman.2018.09.008 · Cited by 83 · see citing works

Liu-Type Logistic Estimator

İnan, D., & Erdoğan, B. E. (2013) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2012.667480 · Cited by 78 · see citing works

Ridge Regression for Solving the Multicollinearity Problem: Review of Methods and Models

Duzan, H. M. B., & Shariff, N. S. B. M. (2015) · Journal of Applied Sciences · doi:10.3923/jas.2015.392.404 · Cited by 77 · see citing works

An incremental extreme learning machine for online sequential learning problems

Guo, L., Hao, J., & Liu, M. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.03.055 · Cited by 73 · see citing works

Multicollinearity Effect in Regression Analysis: A Feed Forward Artificial Neural Network Approach

Obite, C. P., Olewuezi, N. P., Ugwuanyim, G. U., & Bartholomew, D. C. (2020) · Asian Journal of Probability and Statistics · doi:10.9734/ajpas/2020/v6i130151 · Cited by 68 · see citing works

An Efficient Traffic Classification Scheme Using Embedded Feature Selection and LightGBM

Hua, Y. (2020) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc49638.2020.9123302 · Cited by 64 · see citing works

Online sequential extreme learning machine in nonstationary environments

Ye, Y., Squartini, S., & Piazza, F. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.12.064 · Cited by 58 · see citing works

Generalized Cross-Validation for Simultaneous Optimization of Tuning Parameters in Ridge Regression

Roozbeh, M., Arashi, ‎., & Hamzah, N. A. (2020) · Iranian Journal of Science and Technology Transactions A Science · doi:10.1007/s40995-020-00851-1 · Cited by 56 · see citing works

Systems Modelling using Genetic Programming

Willis, M. J., Hiden, H., Hinchliffe, M., McKay, B., & Barton, G. W. (1997) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(97)00206-8 · Cited by 54 · see citing works

A new modified Jackknifed estimator for the Poisson regression model

Türkan, S., & Özel, G. (2015) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1125861 · Cited by 54 · see citing works

TOSELM: Timeliness Online Sequential Extreme Learning Machine

Gu, Y., Liu, J., Chen, Y., Jiang, X., & Yu, H. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.02.047 · Cited by 52 · see citing works

A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Qaraad, M., Amjad, S., Manhrawy, I. I. M., Fathi, H., Hassan, B. A., & Elkafrawy, P. (2021) · IEEE Access · doi:10.1109/access.2021.3065341 · Cited by 51 · see citing works

Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem

Garg, A., & Tai, K. (2012) · DR-NTU (Nanyang Technological University) · Cited by 50 · see citing works

A two-parameter estimator in the negative binomial regression model

Huang, J., & Yang, H. (2012) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2012.696648 · Cited by 43 · see citing works

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

Singh, S. G., & Kumar, S. V. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-05213-y · Cited by 41 · see citing works

BEST SUBSET SELECTION FOR ELIMINATING MULTICOLLINEARITY

Tamura, R., Kobayashi, K., Takano, Y., Miyashiro, R., Nakata, K., & Matsui, T. (2017) · Journal of the Operations Research Society of Japan · doi:10.15807/jorsj.60.321 · Cited by 40 · see citing works

A sequential learning method with Kalman filter and extreme learning machine for regression and time series forecasting

Nobrega, J. P., & Oliveira, A. L. I. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.070 · Cited by 38 · see citing works

A Recursive Least-Squares with a Time-Varying Regularization Parameter

Mahadi, M., Ballal, T., Moinuddin, M., & Al-Saggaf, U. M. (2022) · Applied Sciences · doi:10.3390/app12042077 · Cited by 24 · see citing works

Multicollinearity in regression: Review and examples

Askin, R. G. (1982) · Journal of Forecasting · doi:10.1002/for.3980010307 · Cited by 22 · see citing works

A MORE GENERAL CRITERION FOR SUBSET SELECTION IN MULTIPLE LINEAR REGRESSION

Kashid, D. N., & Kulkarni, S. R. (2002) · Communication in Statistics- Theory and Methods · doi:10.1081/sta-120003653 · Cited by 22 · see citing works

Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism

Zhang, X., Liu, S., & Zheng, X. (2021) · Mathematics · doi:10.3390/math9080800 · Cited by 20 · see citing works

Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Kim, J., Wang, N., Liu, Y., & Park, K. (2020) · Symmetry · doi:10.3390/sym12030381 · Cited by 19 · see citing works

Partial ridge regression under multicollinearity

Chandrasekhar, C. K., Bagyalakshmi, H., Srinivasan, M. R., & Gallo, M. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1181726 · Cited by 18 · see citing works

High‐dimensional variable screening under multicollinearity

Zhao, N., Xu, Q., Tang, M., Jiang, B., Chen, Z., & Wang, H. (2020) · Stat · doi:10.1002/sta4.272 · Cited by 18 · see citing works

Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms

Larabi-Marie-Sainte, S. (2021) · Applied Sciences · doi:10.3390/app11156769 · Cited by 16 · see citing works

Improved high-dimensional regression models with matrix approximations applied to the comparative case studies with support vector machines

Roozbeh, M., Babaie–Kafaki, S., & Aminifard, Z. (2022) · Optimization methods & software · doi:10.1080/10556788.2021.2022144 · Cited by 15 · see citing works

Variable selection under multicollinearity using modified log penalty

Nguyen, V. C., & Ng, C. T. (2019) · Journal of Applied Statistics · doi:10.1080/02664763.2019.1637829 · Cited by 14 · see citing works

Stress test procedure for feature selection algorithms

Katrutsa, A., & Strijov, V. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.01.018 · Cited by 13 · see citing works

A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations

Arashi, ‎., Norouzirad, M., Roozbeh, M., & Khan, N. M. (2021) · Mathematics · doi:10.3390/math9233057 · Cited by 11 · see citing works

Biased estimators in Poisson regression model in the presence of multicollinearity: A subject review

Algamal, Z. Y. (2018) · AL-Qadisiyah Journal For Administrative and Economic sciences · Cited by 10 · see citing works

A Comparative Study on Regression Methods in the presence of Multicollinearity

Toka, O. (2016) · DergiPark (Istanbul University) · Cited by 9 · see citing works

A nonlinear mixed–integer programming approach for variable selection in linear regression model

Roozbeh, M., Babaie–Kafaki, S., & Aminifard, Z. (2021) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2021.1990323 · Cited by 9 · see citing works

Symbolic regression in multicollinearity problems

Castillo, F. A., & Villa, C. M. (2005) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1068009.1068377 · Cited by 3 · see citing works

Multicollinearity problem of CPM communication signals and its suppression method with PLS algorithm

Li, C., Wang, H., Wang, J., Tai, Y., & Yang, F. (2018) · ACM International Conference on Underwater Networks & Systems (WUWNet) · doi:10.1145/3291940.3291980 · Cited by 3 · see citing works

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Rahman, A., Debnath, T., Kundu, D., Khan, M. S. I., Aishi, A. A., Sazzad, S., Sayduzzaman, M., & Band, S. S. (2024) · AIMS Public Health · doi:10.3934/publichealth.2024004 · Cited by 251 · see citing works

How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

Jiang, S., Sweet, L., Blougouras, G., Brenning, A., Li, W., Reichstein, M., Denzler, J., Shangguan, W., Yu, G., Huang, F., et al. (2024) · Earth s Future · doi:10.1029/2024ef004540 · Cited by 198 · see citing works

A comparative analysis of multivariate approaches for data analysis in management sciences

Ahmed, R. R., Štreimikienė, D., Štreimikis, J., & Šikšnelytė-Butkienė, I. (2024) · E+M Ekonomie a Management · doi:10.15240/tul/001/2024-5-001 · Cited by 123 · see citing works

Extubation in neurocritical care patients: the ENIO international prospective study

Cinotti, R., Mijangos, J., Pelosi, P. P., Hænggi, M., Gurjar, M., Schultz, M. J., Kaye, C. T., Godoy, D. A., Alvarez, P., Ioakeimidou, A., et al. (2022) · Intensive Care Medicine · doi:10.1007/s00134-022-06825-8 · Cited by 121 · see citing works

Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets

Ganie, S. M., Pramanik, P. K. D., & Zhao, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-97547-6 · Cited by 115 · see citing works

Machine learning can guide food security efforts when primary data are not available

Martini, G., Bracci, A., Riches, L., Jaiswal, S., Corea, M., Rivers, J., Husain, A., & Omodei, E. (2022) · Nature Food · doi:10.1038/s43016-022-00587-8 · Cited by 110 · see citing works

Deep learning for wildfire risk prediction: Integrating remote sensing and environmental data

Xu, Z., Li, J., Cheng, S. L., Xue, R., Zhao, Y., He, H., Guan, H., Sharma, A., Erxleben, M., Chang, R., et al. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.06.002 · Cited by 100 · see citing works

Interpretability in near-infrared (NIR) spectroscopy: Current pathways to the long-standing challenge

Beć, K. B., Grabska, J., & Huck, C. W. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118254 · Cited by 98 · see citing works

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

Scholkmann, F., Tachtsidis, I., Wolf, M., & Wolf, U. (2022) · Neurophotonics · doi:10.1117/1.nph.9.3.030801 · Cited by 92 · see citing works

A framework for optimizing environmental covariates to support model interpretability in digital soil mapping

Kasraei, B., Schmidt, M., Zhang, J., Bulmer, C., Filatow, D., Arbor, A., Pennell, T., & Heung, B. (2024) · Geoderma · doi:10.1016/j.geoderma.2024.116873 · Cited by 78 · see citing works

Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis

Hoxha, J., Çodur, M. Y., Mustafaraj, E., Kanj, H., & Masri, A. E. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121765 · Cited by 72 · see citing works

The exposome of healthy and accelerated aging across 40 countries

Hernandez, H., Santamaria-Garcia, H., Moguilner, S., Farina, F. R., Legaz, A., Prado, P., Cuadros, J., Gonzalez, L., Gonzalez-Gomez, R., Migeot, J., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03808-2 · Cited by 71 · see citing works

Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

Alyami, M., Khan, M., Javed, M. F., Ali, M., Alabduljabbar, H., Najeh, T., & Gamil, Y. (2023) · Developments in the Built Environment · doi:10.1016/j.dibe.2023.100307 · Cited by 68 · see citing works

How Does the Digital Transformation of Banks Improve Efficiency and Environmental, Social, and Governance Performance?

Zhu, Y., & Jin, S. (2023) · Systems · doi:10.3390/systems11070328 · Cited by 63 · see citing works

Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis

Cechinel, M. A. P., Neves, J., Fuck, J. V. R., de Andrade, R. C., Spogis, N., Riella, H. G., Padoin, N., & Soares, C. (2024) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2023.104758 · Cited by 63 · see citing works

Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices

Ma, X., Guan, D., Zhang, C., Yu, T., Li, C., Wu, Z., Li, B., Geng, W., Wu, T., & Yang, Z. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.135407 · Cited by 62 · see citing works

Deep Learning Cascaded Feature Selection Framework for Breast Cancer Classification: Hybrid CNN with Univariate-Based Approach

Samee, N. M. A., Atteia, G. E., Meshoul, S., Al–antari, M. A., & Kadah, Y. M. (2022) · Mathematics · doi:10.3390/math10193631 · Cited by 59 · see citing works

Nexus between FinTech, renewable energy resource consumption, and carbon emissions

Firdousi, S. F., Afzal, A., & Amir, B. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-28219-z · Cited by 59 · see citing works

Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning

Vellayappan, K., Yue, Y., Lim, K. H., Cao, K., Tan, J. Y., Cheng, S., Wang, T., Gani, T. Z. H., Karimi, I. A., & Kawi, S. (2023) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2023.122593 · Cited by 58 · see citing works

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Samee, N. M. A., Alhussan, A. A., Ghoneim, V. F., Atteia, G. E., Alkanhel, R. I., Al–antari, M. A., & Kadah, Y. M. (2022) · Sensors · doi:10.3390/s22134938 · Cited by 58 · see citing works

Clustering the European Union electric vehicle markets: A scenario analysis until 2035

Möring-Martínez, G., Senzeybek, M., & Jochem, P. E. P. (2024) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2024.104372 · Cited by 51 · see citing works

Prediction of UCS of fine-grained soil based on machine learning part 2: comparison between hybrid relevance vector machine and Gaussian process regression

Khatti, J., & Grover, K. S. (2023) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-023-00191-8 · Cited by 46 · see citing works

Algorithms going wild – A review of machine learning techniques for terrestrial ecology

Cipriano, C., Noce, S., Mereu, S., & Santini, M. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111164 · Cited by 44 · see citing works

Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases

Fittipaldi, S., Legaz, A., Maito, M. A., Hernandez, H., Altschuler, F., Canziani, V., Moguilner, S., Gillan, C. M., Castillo, J., Lillo, P., et al. (2024) · Nature Mental Health · doi:10.1038/s44220-023-00164-3 · Cited by 41 · see citing works

Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology

Fuck, J. V. R., Cechinel, M. A. P., Neves, J., de Andrade, R. C., Tristão, R., Spogis, N., Riella, H. G., Soares, C., & Padoin, N. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.141472 · Cited by 40 · see citing works

The social, economic, and environmental drivers of renewable energy: Is income inequality a threat to renewable energy transition?

Eyüboğlu, K., & Üzar, U. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.144780 · Cited by 40 · see citing works

Computational approaches for predicting variant impact: An overview from resources, principles to applications

Liu, Y., Yeung, W. S. B., Chiu, P. C. N., & Cao, D. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.981005 · Cited by 40 · see citing works

Exploring students' choice of active travel to school in different spatial environments: A case study in a mountain city

Liu, Y., Min, S., Shi, Z., & He, M. (2024) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2024.103795 · Cited by 39 · see citing works

Machine learning for cyanobacteria mapping on tropical urban reservoirs using PRISMA hyperspectral data

Begliomini, F. N., Barbosa, C. C. F., Martins, V. S., Novo, E. M. L. D. M., Paulino, R. S., Maciel, D. A., de Lima, T. M. A., O’Shea, R. E., Pahlevan, N., & Lamparelli, M. C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.09.019 · Cited by 37 · see citing works

Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning

Khatti, J., & Grover, K. S. (2023) · Transportation Infrastructure Geotechnology · doi:10.1007/s40515-023-00357-4 · Cited by 37 · see citing works

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Raja, M. N. A., Abdoun, T. H., & El-Sekelly, W. (2024) · Buildings · doi:10.3390/buildings14040954 · Cited by 37 · see citing works

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Fan, C., Zhang, N., Jiang, B., & Liu, W. V. (2023) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.06.005 · Cited by 36 · see citing works

Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption

Antonio, B.-O., Juan, L.-R., Ana, I.-D., & Francisco, L.-C. (2024) · Financial Innovation · doi:10.1186/s40854-024-00625-3 · Cited by 36 · see citing works

UAV-based multispectral image analytics and machine learning for predicting crop nitrogen in rice

Khose, S. B., & Mailapalli, D. R. (2024) · Geocarto International · doi:10.1080/10106049.2024.2373867 · Cited by 36 · see citing works

A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments

Islam, M. S., Islam, M. I., Mozumder, A. Q., Khan, M. T. H., Das, N., & Mohammad, N. (2025) · Sustainability · doi:10.3390/su17209234 · Cited by 36 · see citing works

Assessment of the uniaxial compressive strength of intact rocks: an extended comparison between machine and advanced machine learning models

Khatti, J., & Grover, K. S. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00408-4 · Cited by 32 · see citing works

Cognitive Risks of AI: Literacy, Trust, and Critical Thinking

Kulal, A. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2582050 · Cited by 32 · see citing works

Predicting determinants influencing user satisfaction with mental health app: An explainable machine learning approach based on unstructured data

Darko, A. P., Antwi, C. O., Adjei, K., Zhang, B., & Ren, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123647 · Cited by 31 · see citing works

Dynamics and causes of cropland Non-Agriculturalization in typical regions of China: An explanation Based on interpretable Machine learning

Zhang, G., Li, X., Zhang, L., & Wei, X. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112348 · Cited by 31 · see citing works

Multi‐sensor and multi‐temporal high‐throughput phenotyping for monitoring and early detection of water‐limiting stress in soybean

Jones, S. E., Ayanlade, T. T., Fallen, B. D., Jubery, T. Z., Singh, A., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. (2024) · The Plant Phenome Journal · doi:10.1002/ppj2.70009 · Cited by 31 · see citing works

Associations between the chemical exposome and pregnancy induced hypertension

Soomro, M. H., England-Mason, G., Liu, J., Reardon, A. J. F., MacDonald, A. M., Kinniburgh, D. W., Martin, J. W., & Dewey, D. (2023) · Environmental Research · doi:10.1016/j.envres.2023.116838 · Cited by 31 · see citing works

Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches

Widya, L. K., Rezaie, F., Lee, W., Lee, C.-W., Nurwatik, N., & Lee, S. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.121291 · Cited by 30 · see citing works

Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals

Yang, L., Zhou, R., Li, G., Yang, Y., & Zhao, Q. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107835 · Cited by 30 · see citing works

Recognition and prediction of elderly thermal sensation based on outdoor facial skin temperature

Wang, J., Li, Q., Zhu, G., Kong, W., Peng, H., & Wei, M. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111326 · Cited by 29 · see citing works

The Multicollinearity Effect on the Performance of Machine Learning Algorithms: Case Examples in Healthcare Modelling

Yıldırım, H. (2024) · Academic Platform Journal of Engineering and Smart Systems · doi:10.21541/apjess.1371070 · Cited by 29 · see citing works

Predicting the prevalence of cardiovascular diseases using machine learning algorithms

Sianga, B. E., Mbago, M. C. Y., & Msengwa, A. S. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100199 · Cited by 29 · see citing works

The Effects of ESG Scores and ESG Momentum on Stock Returns and Volatility: Evidence from U.S. Markets

Escobar-Saldívar, L. J., Villarreal-Samaniego, D., & Santillán‐Salgado, R. J. (2025) · Journal of risk and financial management · doi:10.3390/jrfm18070367 · Cited by 29 · see citing works

Developing statistical and machine learning models for predicting CO2 solubility in live crude oils

Bhattacherjee, R., Botchway, K., Pashin, J. C., Chakraborty, G., & Bikkina, P. K. (2024) · Fuel · doi:10.1016/j.fuel.2024.131577 · Cited by 28 · see citing works

Inequity in accessibility to urban parks in environmental gentrification areas based on Multi-G3SFCA: A case study of Wuhan’s main urban districts

Deng, Z., & Ning, L. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112899 · Cited by 28 · see citing works

Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest

Lamahewage, S. H. G., Witharana, C., Riemann, R. I., Fahey, R. T., & Worthley, T. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-15585-6 · Cited by 28 · see citing works

The Bitcoin Halving Cycle Volatility Dynamics and Safe Haven-Hedge Properties: A MSGARCH Approach

Chan, J. Y.-L., Phoong, S. W., Phoong, S. Y., Cheng, W. K., & Chen, Y. (2023) · Mathematics · doi:10.3390/math11030698 · Cited by 28 · see citing works

Validation of Risk Stratification for Cardiac Events in Pregnant Women With Valvular Heart Disease

Pande, S. N., Suriya, J. Y., Ganapathy, S., Pillai, A. A., Satheesh, S., Mondal, N., Kumar, K. T. H., Silversides, C. K., Siu, S. C., D'Souza, R. D., et al. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.07.023 · Cited by 28 · see citing works

Predicting Food‐Security Crises in the Horn of Africa Using Machine Learning

Busker, T., van den Hurk, B., de Moel, H., van den Homberg, M. V. D., van Straaten, C., Odongo, R. A., & Aerts, J. C. J. H. (2024) · Earth's Future · doi:10.1029/2023ef004211 · Cited by 14 · see citing works

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki, M. T., Rowles, L. S., Adjeroh, D., & Orner, K. D. (2023) · ACS ES&T Engineering · doi:10.1021/acsestengg.3c00043 · Cited by 27 · see citing works

Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete

Alyami, M., Onyelowe, K. C., AlAteah, A. H., Alahmari, T. S., Alsubeai, A., Ullah, I., & Javed, M. F. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e03869 · Cited by 26 · see citing works

Applications of Machine Learning in Cyber Security: A Review

Vourganas, I., & Michala, A. L. (2024) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp4040045 · Cited by 26 · see citing works

Optimized data-driven method to study the self-healing and durability of ultra-high performance concrete

Huang, Z., Cuenca, E., & Ferrara, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110043 · Cited by 26 · see citing works

Importance of fundamental movement skills to predict technical skills in youth grassroots soccer: A machine learning approach

Duncan, M. J., Eyre, E. L. J., Clarke, N. D., Hamid, A., & Jing, Y. (2023) · International Journal of Sports Science & Coaching · doi:10.1177/17479541231202015 · Cited by 26 · see citing works

XGBoost model for the quantitative assessment of stress corrosion cracking

Zuniga, A. R., Bakhtiari, S. R., Aldrich, C., Calo, V. M., & Iannuzzi, M. (2024) · npj Materials Degradation · doi:10.1038/s41529-024-00538-7 · Cited by 25 · see citing works

Digital readiness across the European Union: A multidimensional empirical assessment of structural determinants, intra-regional disparities, and strategic implications in the era of Industry 5.0

Valášková, K., Juracka, D., Nagy, M., & Chaudhuri, S. (2025) · Equilibrium Quarterly Journal of Economics and Economic Policy · doi:10.24136/eq.3886 · Cited by 25 · see citing works

The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach

Cheng, L. T. W., Cheong, T. S., Wojewodzki, M., & Chui, D. K. H. (2024) · Research in International Business and Finance · doi:10.1016/j.ribaf.2024.102616 · Cited by 24 · see citing works

Long-term prediction of Arctic sea ice concentrations using deep learning: Effects of surface temperature, radiation, and wind conditions

Kim, Y. J., Kim, H., Han, D., Stroeve, J. C., & Im, J. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114568 · Cited by 24 · see citing works

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

Cheng, W. K., Bea, K. T., Leow, S. M. H., Chan, J. Y.-L., Hong, Z., & Chen, Y. (2022) · Mathematics · doi:10.3390/math10142437 · Cited by 24 · see citing works

Machine learning approach for the detection of vitamin D level: a comparative study

Sancar, N., & Tabrizi, S. S. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02323-z · Cited by 24 · see citing works

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Santos, V. O., Guimarães, B. M. D. M., Neto, I. E. L., Filho, F. D. A. D. S., Rocha, P. A. C., Thé, J. V. G., & Gharabaghi, B. (2024) · Remote Sensing · doi:10.3390/rs16111870 · Cited by 24 · see citing works

Assessment of hydraulic conductivity of compacted clayey soil using artificial neural network: an investigation on structural and database multicollinearity

Khatti, J., & Grover, K. S. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01336-0 · Cited by 24 · see citing works

Assessment of Uniaxial Strength of Rocks: A Critical Comparison Between Evolutionary and Swarm Optimized Relevance Vector Machine Models

Khatti, J., & Grover, K. S. (2024) · Transportation Infrastructure Geotechnology · doi:10.1007/s40515-024-00433-3 · Cited by 23 · see citing works

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee

Nascimento, M., Nascimento, A. C. C., Azevedo, C. F., de Oliveira, A. C. B., Caixeta, E. T., & Jarquín, D. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1373318 · Cited by 23 · see citing works

Mechanical–data-driven performance interpretation and design of concrete-filled steel tubes under lateral impact

Liu, K., Wang, J., Gao, S., & Xu, M. (2025) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2025.110204 · Cited by 23 · see citing works

Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis

Zhang, J., van Witteloostuijn, A. C., Zhou, C., & Zhou, S. (2024) · Journal of World Business · doi:10.1016/j.jwb.2024.101517 · Cited by 23 · see citing works

Exploring Learner Engagement in E-Learning Environments: A Predictive Analytics Perspective

Mikić, V., Kekovic, G., Mangaroska, K., Ilić, M., Kopanja, L., & Vesin, B. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2535530 · Cited by 22 · see citing works

Improved Regression Analysis with Ensemble Pipeline Approach for Applications across Multiple Domains

Banik, D., Paul, R., Rathore, R. S., & Jhaveri, R. H. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3645110 · Cited by 22 · see citing works

Linear Regression Approach to Solving Multicollinearity and Overfitting in Predictive Analysis

Otse, E. J., Obunadike, G. N., & Abubakar, A. (2025) · Journal of Science Research and Reviews · doi:10.70882/josrar.2025.v2i1.35 · Cited by 21 · see citing works

A random forest and SHAP-based analysis of motorcycle crash severity in Thailand: Urban-rural and day-night perspectives

Sum, S., Se, C., Champahom, T., Jomnonkwao, S., Sinha, S., & Ratanavaraha, V. (2025) · Transportation Engineering · doi:10.1016/j.treng.2025.100369 · Cited by 21 · see citing works

Exploring the main and interactive effects of urban morphology on land surface temperature across different functional zones: A local geoshapley analysis based on XAI

Yang, J.-T., Zhou, X.-W., Chen, X., Yao, X., Li, M., Yin, M.-H., Dong, F., Yan, S.-H., & Zhao, J.-L. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106913 · Cited by 21 · see citing works

Analysis of the building occupancy estimation and prediction process: A systematic review

Caballero-Peña, J., Osma-Pinto, G. A., Rey, J. M., Nagarsheth, S. H., Henao, N., & Agbossou, K. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114230 · Cited by 21 · see citing works

Machine learning-aided cloud analysis for seismic fragility assessment of multi-span bridges

Parisi, F., Nettis, A., & Uva, G. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121175 · Cited by 20 · see citing works

Flood Susceptibility Assessment Using Multi-Tier Feature Selection and Ensemble Boosting Machine Learning Models

Ajin, R. S., Costache, R. D., Bărbulescu, A., Fanti, R., & Segoni, S. (2025) · Water · doi:10.3390/w17142041 · Cited by 20 · see citing works

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Rocha, P. A. C., Santos, V. O., Thé, J. V. G., & Gharabaghi, B. (2023) · Environments · doi:10.3390/environments10120217 · Cited by 20 · see citing works

Machine learning-assisted characterization of the thermal conductivity of cement-based grouts for borehole heat exchangers

Zhao, J. L., Fan, C., Huang, G., Guo, Y., Arachchilage, C. B., Gupta, R. P., & Liu, W. V. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.138506 · Cited by 19 · see citing works

Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process

Latif, R. M. A., & He, J. (2024) · Atmosphere · doi:10.3390/atmos16010022 · Cited by 18 · see citing works

Performance of Ridge Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net in Overcoming Multicollinearity

Saputro, D. R. S., Wahyu, N. L., & Widyaningsih, Y. (2025) · Journal of Multidisciplinary Applied Natural Science · doi:10.47352/jmans.2774-3047.251 · Cited by 18 · see citing works

Exploring the adsorption sites and mechanism of biochar towards tetracycline using machine learning

Xian, B., Li, Q., Zhao, H., & Gong, Q. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.107970 · Cited by 18 · see citing works

Machine learning for the prediction of proteolysis in Mozzarella and Cheddar cheese

Golzarijalal, M., Ong, L., Neoh, C. R., Harvie, D. J. E., & Gras, S. L. (2024) · Food and Bioproducts Processing · doi:10.1016/j.fbp.2024.01.009 · Cited by 18 · see citing works

Failure mode identification in reinforced concrete flat slabs using advanced ensemble neural networks

Barkhordari, M. S., Fattahi, H., Armaghani, D. J., Khan, N. M., Afrazi, M., & Asteris, P. G. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00554-9 · Cited by 18 · see citing works

An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data

Auzzas, A., Capra, G. F., Jani, A. D., & Ganga, A. (2024) · Modeling Earth Systems and Environment · doi:10.1007/s40808-024-02127-8 · Cited by 18 · see citing works

Nonparametric regression estimator of multivariable Fourier Series for categorical data

Zulfadhli, M., Budiantara, I. N., & Ratnasari, V. (2024) · MethodsX · doi:10.1016/j.mex.2024.102983 · Cited by 18 · see citing works

Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis

Kalhori, S. R. N., Najafi, F., Hasannejadasl, H., & Heydari, S. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105804 · Cited by 18 · see citing works

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Santos, V. O., Rocha, P. A. C., Gharabaghi, B., & Thé, J. V. G. (2023) · Environments · doi:10.3390/environments10090157 · Cited by 17 · see citing works

Using Machine Learning to Predict Nonachievement of Clinically Significant Outcomes After Rotator Cuff Repair

Alaiti, R. K., Vallio, C. S., Assunção, J. H., Silva, F. B. D. A. E., Gracitelli, M. E. C., Neto, A. A. F., & Malavolta, E. A. (2023) · Orthopaedic Journal of Sports Medicine · doi:10.1177/23259671231206180 · Cited by 17 · see citing works

How false data affects machine learning models in electrochemistry?

Deshsorn, K., Lawtrakul, L., & Iamprasertkun, P. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234127 · Cited by 17 · see citing works

Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations

Chen, Q., Shao, K., & Zhang, S. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.122107 · Cited by 17 · see citing works

Predicting Cross-border Merger and Acquisition Completion through CEO Characteristics: A Machine Learning Approach

Cheng, C., & Dai, J. (2024) · Management International Review · doi:10.1007/s11575-024-00562-4 · Cited by 17 · see citing works

Estimation of Uniaxial Strength of Rock: A Comparison between Bayesian-Optimized Machine Learning Models

Khatti, J., & Grover, K. S. (2024) · Mining Metallurgy & Exploration · doi:10.1007/s42461-024-01168-y · Cited by 17 · see citing works

Machine learning predicts selectivity of green synthesized iron nanoparticles toward typical contaminants: critical factors in synthesis conditions, material properties, and reaction process

Xiao, Y., Zhang, Z., Lin, J., Chen, W., Huang, J., & Chen, Z. (2025) · Environmental Research · doi:10.1016/j.envres.2025.121605 · Cited by 16 · see citing works

An explainable machine learning system for left bundle branch block detection and classification

Ordóñez, B. D. C. M., Garrigós, J., Martínez, J. J., Vicente, J. M. F., & Bonomini, M. P. (2023) · Integrated Computer-Aided Engineering · doi:10.3233/ica-230719 · Cited by 16 · see citing works

Renewable Energy, GDP (Gross Domestic Product), FDI (Foreign Direct Investment) and CO2 Emissions in Southeast Asia Countries

Ngoc, T. (2023) · International Journal of Energy Economics and Policy · doi:10.32479/ijeep.14022 · Cited by 15 · see citing works

The quantification of surface abrasion on flint stone tools

Bustos‐Pérez, G., & Ollé, A. (2023) · Archaeometry · doi:10.1111/arcm.12913 · Cited by 15 · see citing works

Development of comprehensive prediction models for pumpkin fruit sensory quality using physicochemical analysis, near-infrared spectroscopy, and machine learning

Xu, Y., Luo, J., Xue, S., Jin, Q., Zhu, J., Lu, S., Meng, Q., Du, H., Fu, M., & Zhong, Y. (2024) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2024.106530 · Cited by 15 · see citing works

China's progress in synergetic governance of climate change and multiple environmental issues

Yang, J., Zhao, Z., Fang, W., Ma, Z., Liu, M., & Bi, J. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae351 · Cited by 15 · see citing works

Multicollinearity In Machine Learning: Detection, Impact, And Remedial Techniques

Reddy, D. M. C., & Naresh, D. M. (2026) · International Journal of Advances in Signal and Image Sciences · doi:10.29284/sg8jdw24 · Cited by 1 · see citing works

Comparison of statistical and machine learning methods in modelling of data with multicollinearity

Garg, A., & Tai, K. (2013) · International Journal of Modelling Identification and Control · doi:10.1504/ijmic.2013.053535 · Cited by 154 · see citing works

An Alternative Multicollinearity Approach in Solving Multiple Regression Problem

Zainodin, H. J., Noraini, A., & Yap, S. J. (2011) · Trends in Applied Sciences Research · doi:10.3923/tasr.2011.1241.1255 · Cited by 49 · see citing works

Review of Classical Methods and Variables Selection in Case of Multicollinearity: A Case Study with Real-Data

Barakat, S. L. (2023) · المجلة العملیة التجارة والتمویل · doi:10.21608/caf.2023.336191 · Cited by 3 · see citing works

Regresion analysis with multicollinear predictor variables: definition, derection, and effects

Gunst, R. F. (1983) · Communication in Statistics- Theory and Methods · doi:10.1080/03610928308828603 · Cited by 81 · see citing works