Supersparse Linear Integer Models for Optimized Medical Scoring Systems

2015-02-15 · Berk Ustun, Cynthia Rudin

Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints. We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Model (SLIM). SLIM scoring systems are built by solving an integer program that directly encodes measures of accuracy (the 0-1 loss) and sparsity (the $\ell_0$-seminorm) while restricting coefficients to coprime integers. SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce highly tailored models without parameter tuning. We provide bounds on the testing and training accuracy of SLIM scoring systems, and present a new data reduction technique that can improve scalability by eliminating a portion of the training data beforehand. Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM was used to create a highly tailored scoring system for sleep apnea screening

📄 PDF Abstract BibTeX arXiv:1502.04269

Code (2)

ustunb/slim_for_matlab 공식 구현

csinva/imodels

Tasks

Interpretable Machine Learning

Supersparse Linear Integer Models for Predictive Scoring Systems

2013-06-25 · Berk Ustun, Stefano Traca, Cynthia Rudin

We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification. We derive theoretical bounds on the true risk of SLIM scoring systems, and present experimental results…

Binary Classification Classification General Classification

Supersparse Linear Integer Models for Interpretable Classification

2013-06-27 · Berk Ustun, Stefano Tracà, Cynthia Rudin

Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because they are practical and interpretable. In …

Classification General Classification

Learning Optimal Fair Scoring Systems for Multi-Class Classification

2023-04-11 · Julien Rouzot, Julien Ferry, Marie-José Huguet

Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, there are growing concerns about these mode…

Binary Classification Classification Decision Making Fairness+1

MISS: Multiclass Interpretable Scoring Systems

2024-01-10 · Michal K. Grzeszczyk, Tomasz Trzciński, Arkadiusz Sitek

In this work, we present a novel, machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) - a fully data-driven methodology for generating single, sparse, and user-friendly scoring syst…

Binary Classification Classification Dimensionality Reduction

Buffered AUC maximization for scoring systems via mixed-integer optimization

2026-01-09 · Moe Shiina, Shunnosuke Ikeda, Yuichi Takano arxiv

A scoring system is a linear classifier composed of a small number of explanatory variables, each assigned a small integer coefficient. This system is highly interpretable and allows predictions to be made with simple ma…

Binary Classification