What does this research mean for the field?

Inverse-based Lasso regression algorithms (Lasso-I and Lasso-H) effectively perform feature selection and mitigate multicollinearity without requiring explicit regularization parameter specification. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate novel inverse-based optimization algorithms for Lasso regression parameter estimation that perform feature selection and mitigate multicollinearity without manual penalty tuning.

August 23, 2026Intelligenza Artificiale

Inverse-based Lasso Regression: Novel Algorithms for Feature Selection and Multicollinearity Mitigation

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Authors

EGEkaterina GribanovaTomsk State University of Control Systems and Radio-Electronics RGRoman GerasimovTomsk State University of Control Systems and Radio-Electronics

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Implication

Benchmarking analysis demonstrates inverse-based Lasso algorithms optimize parameter estimation in high-dimensional datasets, indicating robust feature selection without penalty tuning.

Key Points

To develop and evaluate novel inverse-based optimization algorithms for Lasso regression parameter estimation that perform feature selection and mitigate multicollinearity without manual penalty tuning.
Reformulated Lasso parameter estimation as an inverse single-point optimization problem, introducing a coordinate descent approach (Lasso-I) and an information-criteria wrapper hybrid (Lasso-H).
Evaluated algorithm performance across linear and logistic regression models using benchmark datasets from the KEEL and UCI repositories.
Assessed model performance using Akaike Information Criterion (AIC), Mean Squared Error (MSE), and R² metrics.
Lasso-H achieved optimal AIC values in 90% of the evaluated logistic regression dataset cases.
Both algorithms eliminated the need for explicit regularization parameter tuning while effectively mitigating multicollinearity and maintaining feature selection accuracy across high-dimensional data.

Cite This Study

Gribanova et al. (2026) studied this question.

synapsesocial.com/papers/6a8aad857677a34114445a0b https://doi.org/10.1177/17248035261480406

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Implication

Benchmarking analysis demonstrates inverse-based Lasso algorithms optimize parameter estimation in high-dimensional datasets, indicating robust feature selection without penalty tuning.

Inverse-based Lasso Regression: Novel Algorithms for Feature Selection and Multicollinearity Mitigation

Discussion

Member takes

Implication

Key Points

Cite This Study

Inverse-based Lasso Regression: Novel Algorithms for Feature Selection and Multicollinearity Mitigation

Discussion

Member takes

Implication

Key Points

Cite This Study

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