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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review
Open accessAbstract
Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored. Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem. The two main approaches used to mitigate multicollinearity are variable selection methods and modified estimator methods. However, variable selection methods may negate efforts to collect more data as new data may eventually be dropped from modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to better mitigate multicollinearity.
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“In high-dimensional datasets, redundancy and multicollinearity among input features are frequently observed [52].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [52]
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Metadata
- Authors
- Chan, J. Y.-L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z.-W., & Chen, Y.-L.
- Affiliations
- Universiti Tunku Abdul Rahman (MY)
- University of Malaya (MY)
- Feng Chia University (TW)
- National Taipei University of Technology (TW)
- Corresponding author
- Chan, Jireh Yi-Le
- Chen, Yen-Lin
- Venue
- Mathematics
- Publisher
- MDPI AG
- Published
- 2022-04-12
- Volume
- 10
- Issue
- 8
- Pages
- 1283
- ISSN
- 2227-7390 (ISSN-L)
- OpenAlex
- W4223552619
- Cited by
- 617
- References
- 70
- Citations by year
- 2026: 165 · 2025: 216 · 2024: 156 · 2023: 64 · 2022: 16
- FWCI
- 83.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Mathematics (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology of Taiwan (109-2628-E-027-004–MY3, MOST-110-2218-E-027-004)
- Ministry of Higher Education (FRGS/1/2019/STG06/UTAR/03/1)
- Ministry of Education of Taiwan (Official Document No. 1100156712)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- multicollinearity, feature selection, modified estimators, big data, high-dimensional data
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