Feature subset selection for logistic regression via mixed integer optimization

By Toshiki Sato, Yuichi Takano, Ryuhei Miyashiro, Akiko Yoshise. 2016

58 citations recorded by the source corpus.

Subfields: Statistics and Probability

How researchers see this paper

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

In Statistics and Probability

1. Cynthia Rudin
2. Berk Ustun
3. Ryuhei Miyashiro
4. Yuichi Takano
5. Toshiki Sato
6. Nadia Burkart
7. Marco F. Huber
8. Tong Wang

Citations in the corpus

• Learning Optimized Risk Scores (2016, background): “Our work is part of a recent stream of research on integer programming in supervised learning (Carrizosa et al., 2016; Liu and Wu, 2007; Goldberg and Eckstein, 2012; Guan et al., 2009; Nguyen and Franke, 2012; Sato et al., 2015, 2016; Bertsimas et al., 2016; Bertsimas and Dunn, 2017).”
• Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model (2015, methodology): “…on quadratic approximation, PWL MILO formulation (3.9)–(3.12) based on piecewise-linear approximation using the following set of points for tangent lines, similarly to Sato et al. [29]: All computations were performed on a Linux computer with an Intel Core i7-4820 CPU (3.70 GHz) and 32 GB memory.”
• Optimized Risk Scores (2017, methodology): “Our algorithm may be used to solve other problems with non-convex penalties and constraints, such as `0-regularized risk minimization [27], or discrete linear classi€cation problems that minimize a convex loss over a small integers [4, 7, 24, 35].”