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Cited by 659Advanced Statistical Methods and ModelsStatistical Methods and ApplicationsStatistical Methods and Inference

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

Open access
Chan, Jireh Yi-Le, Leow, Steven Mun Hong, Bea, Khean Thye, Cheng, Wai Khuen, Phoong, Seuk Wai, Hong, Zeng-Wei, Chen, Yen-Lin·2022·Mathematics
DOI 10.3390/math10081283
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Abstract

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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Research works printed but not linked: 11

  1. Bollinger, 1992, Using bollinger bands, Stock. Commod., 10, 47
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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
ORCID
Venue
Mathematics
Publisher
MDPI AG
Published
2022-04-12
Volume
10
Issue
8
Pages
1283
ISSN
2227-7390 (ISSN-L)
DOI
10.3390/math10081283
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
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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