Mitigating the multicollinearity problem and its machine learning approach: a review
Document Type
Article
Publication Date
4-1-2022
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.
Keywords
Multicollinearity, Variable selection methods, Optimization approaches, Neural network, Machine learning
Divisions
Faculty_of_Business_and_Accountancy
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]
Publication Title
Mathematics
Volume
10
Issue
8
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND