Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model

By Paul Brooks, Eva K. Lee. 2010

40 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cynthia Rudin
2. Berk Ustun
3. J. Paul Brooks
4. Eva K. Lee
5. Tong Wang
6. Lesia Semenova
7. Chudi Zhong
8. Zhi Chen

In Health Information Management

1. Berk Ustun
2. Cynthia Rudin
3. J. Paul Brooks

Citations in the corpus

• Supersparse linear integer models for optimized medical scoring systems (2015, background): “Convex surrogate loss functions, for instance, produce models that do not attain the best learning-theoretic guarantee on predictive accuracy and are not robust to outliers (Li and Lin 2007; Brooks and Lee 2010; Nguyen and Sanner 2013).”
• Methods and Models for Interpretable Linear Classification (2014, methodology): “Recent progress in commercial MIP software has made it possible to solve exponentially larger problems [13, 12], and ushered in new MIP classification models that involve feature selection [42, 45, 46, 84], or the creation of a reserved-judgement region [22, 21].”
• Support Vector Machines with the Ramp Loss and the Hard Margin Loss (2011, background): “Brooks and Lee [ 7 ] prove that an integer-programming based method for constrained discrimination, a generalization of the classification problem, is consistent.”
• Systems Biology of Seasonal Influenza Vaccination in Humans (2011, methodology): “For this, we used DAMIP (discriminant analysis via mixed integer programmin”