Automated Model Selection for Generalized Linear Models
Benjamin Schwendinger, Florian Schwendinger, Laura Vana-Gür
Abstract
In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models like Ridge regression and the OSCAR model.
Related Papers
Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression2025-07-20mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection2025-07-17Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts2025-07-16Imbalanced Regression Pipeline Recommendation2025-07-16Second-Order Bounds for [0,1]-Valued Regression via Betting Loss2025-07-16Sparse Regression Codes exploit Multi-User Diversity without CSI2025-07-15Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection2025-07-15Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning2025-07-14