2018/08/07 by Ana Kenney, Kenney, Ana, Francesca Chiaromonte +3
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #Methodology (stat.ME) #Multi-Criteria Decision Making #Optimization and Mathematical Programming #Sparse and Compressive Sensing Techniques #stat.ME
paper · pdf · doi:10.48550/arxiv.1808.02526
This work has been presented at JSM 2018 (Vancouver, Canada), ISNPS 2018 (Salerno, Italy), and various other conferences
openalex publication_date 2018/08/07 · arxiv created 2019/09/30 · arxiv updated 2019/10/01 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
Recent advances in mathematical programming have made Mixed Integer Optimization a competitive alternative to popular regularization methods for selecting features in regression problems. The approach exhibits unquestionable foundational appeal and versatility, but also poses important challenges. Here we propose MIP-BOOST, a revision of standard Mixed Integer Programming feature selection that reduces the computational burden of tuning the critical sparsity bound parameter and improves performance in the presence of feature collinearity and of signals that vary in nature and strength. The final outcome is a more efficient and effective L0 Feature Selection method for applications of realistic size and complexity, grounded on rigorous cross-validation tuning and exact optimization of the associated Mixed Integer Program. Computational viability and improved performance in realistic scenarios is achieved through three independent but synergistic proposals.