Visual Analytics of Multicollinearity Mitigation Techniques Using Correlation, VIF and Regularization Based Feature Selection
Journal of Computer and Communication Networks
Received On : 26 October 2025
Revised On : 02 January 2026
Accepted On : 26 January 2026
Published On : 30 January 2026
Volume 02, 2026
Pages : 025-036
Abstract
One of the worst issues with high-dimensional data is that it leads to multicollinearity, which results in unstable model coefficients, reduced interpretability, and weak predictive diagnostics. In this paper, a feature optimization framework will be introduced, wherein methods sensitive to multicollinearity are applied to combine statistical filtering, iterative elimination, and regularization in a single pipeline. Synthetic data in a systematic designed way has manipulated factors of correlation of features, redundancy and noise such that mitigation methods can be tested in controlled conditions. The suggested design will consist of preprocessing where the data will undergo Z-score normalization and a missing data processing step and correlation-based filtering which will eliminate very correlated pairs of features. The feature space is then further reduced by dropping redundant variables beyond specified thresholds by repeatedly using Variance Inflation Factor (VIF). Ridge (L2) and Lasso (L1) regularization techniques are provided to enforce more strength which in fact stabilizes coefficients and helps in sparsity. A refined feature set is then subjected to an orthogonal space by way of Principal Component Analysis (PCA) in order to minimize redundancy as much as possible. The overall experimental study demonstrates that the proposed solution will assist in reducing the degree of multicollinearity to a minimum, since the value of VIF will reduce over 85 percent and the stability of the model can also be improved. It can be seen via comparative analysis that, the RMSE has been reduced by approximately 18 – 25% and the R2 has been increased up to 12% in comparison to those of the base models where no feature optimization has taken place. By incorporating various mitigation tactics, interpretability and predictive accuracy are guaranteed, and as such, the framework can be used in real-world contexts in which high-dimensional data are frequently applicable.
Keywords
Multicollinearity, Feature Selection, Variance Inflation Factor (VIF), Regularization, Principal Component Analysis (PCA), Regression Analysis.
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Aisling Yue Irwing, “Visual Analytics of Multicollinearity Mitigation Techniques Using Correlation, VIF and Regularization Based Feature Selection”, Journal of Computer and Communication Networks, pp. 025-036, 2026, doi: 10.64026/JCCN/2026003.