# Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

arXiv: https://arxiv.org/abs/1510.05417 · HTML: https://mlsanity.com/paper/1510.05417/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Fast Sparse Classification for Generalized Linear and Additive Models](https://mlsanity.com/paper/2202.11389/) (2022)
- [Feature subset selection for kernel SVM classification via mixed-integer optimization](https://mlsanity.com/paper/2205.14325/) (2022)
- [Towards Symbolic Time Series Representation Improved by Kernel Density Estimators](https://mlsanity.com/paper/2205.12960/) (2022)

## Abstract (arXiv)

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.