Mitigating the multicollinearity problem and its machine learning approach: a review

Chan, Jireh Yi-Le and Leow, Steven Mun Hong and Bea, Khean Thye and Cheng, Wai Khuen and Phoong, Seuk Wai and Hong, Zeng-Wei and Chen, Yen-Lin (2022) Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics, 10 (8). ISSN 2227-7390, DOI

Full text not available from this repository.


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.

Item Type: Article
Funders: Fundamental Research Grant Scheme by the Ministry of Higher Education of Malaysia [Grant No: FRGS/1/2019/STG06/UTAR/03/1], Ministry of Science and Technology, Taiwan [Grant No: MOST-109-2628-E-027-004-MY3 & MOST-110-2218-E-027-004], Ministry of Education, Taiwan [Grant No: 1100156712]
Uncontrolled Keywords: Multicollinearity; Variable selection methods; Optimization approaches; Neural network; Machine learning
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Business and Economics
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 29 Sep 2023 03:22
Last Modified: 29 Sep 2023 03:22

Actions (login required)

View Item View Item