MIP-BOOST: Efficient and Effective L 0 Feature Selection for Linear Regression

Ana Kenney, Francesca Chiaromonte, Giovanni Felici

Research output: Contribution to journalArticlepeer-review

Abstract

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 L 0 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. Supplementary materials including additional results, pseudocode, and computer code are available online.

Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'MIP-BOOST: Efficient and Effective L <sub>0</sub> Feature Selection for Linear Regression'. Together they form a unique fingerprint.

Cite this