Mathematics· 2022Q2· Review
Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review
- 589citations
- Q2SCImago
- 2022year
Short summary
Machine learning optimization approaches handle multicollinearity better than statistical estimators, offering an updated strategy for big data analysis.
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Key points
- Multicollinearity is a major problem in big data analysis due to increased variable numbers.
- Traditional methods like variable selection can discard valuable data.
- Machine learning optimization approaches are more effective than statistical estimators for multicollinearity.
- The review traces the evolution of multicollinearity mitigation techniques.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Mathematics, 2022 · DOI ↗
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Field: Analytical Chemistry
Analytical ChemistryChemistry