Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model
By Toshiki Sato, Yuichi Takano, Ryuhei Miyashiro. 2015
27 citations recorded by the source corpus.
This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between the logistic loss function and its quadratic approximation, their formulation may fail to find a good subset of features. To overcome this drawback, we apply a piecewise-linear approximation to the logistic loss function. Accordingly, we frame the feature subset selection problem of minimizing an information criterion as a mixed integer linear optimization problem. The computational results demonstrate that our piecewise-linear approximation approach found a better subset of features than the quadratic approximation approach.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research
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
- Cynthia Rudin
- Berk Ustun
- Akiko Yoshise
- Ryuhei Miyashiro
- Yuichi Takano
- Toshiki Sato
- Tong Wang
- Lesia Semenova
In Computer Vision and Pattern Recognition
In Management Science and Operations Research
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).”
- Feature subset selection for logistic regression via mixed integer optimization (2016, background): “Another direction of future research will be to extend our MILO formulation to discrete choice models, such as the logit model [29] (see, e.g., [36]).”
- BEST SUBSET SELECTION FOR ELIMINATING MULTICOLLINEARITY (2017, background): “Keywords: Optimization, statistics, subset selection, multicollinearity, linear regression, mixed integer semidefinite optimization”