Feature Subset Selection for Ordered Logit Model via Tangent-Plane-Based Approximation

DOI Web Site 6 Citations 23 References Open Access
  • NAGANUMA Mizuho
    Graduate School of Informatics and Engineering, The University of Electro-Communications
  • TAKANO Yuichi
    Faculty of Engineering, Information and Systems, University of Tsukuba
  • MIYASHIRO Ryuhei
    Institute of Engineering, Tokyo University of Agriculture and Technology

Search this article

Description

<p>This paper is concerned with a mixed-integer optimization (MIO) approach to selecting a subset of relevant features from among many candidates. For ordinal classification, a sequential logit model and an ordered logit model are often employed. For feature subset selection in the sequential logit model, Sato et al.[22] recently proposed a mixed-integer linear optimization (MILO) formulation. In their MILO formulation, a univariate nonlinear function contained in the sequential logit model was represented by a tangent-line-based approximation. We extend this MILO formulation toward the ordered logit model, which is more commonly used for ordinal classification than the sequential logit model is. Making use of tangent planes to approximate a bivariate nonlinear function involved in the ordered logit model, we derive an MILO formulation for feature subset selection in the ordered logit model. Our computational results verify that the proposed method is superior to the L1-regularized ordered logit model in terms of solution quality.</p>

Journal

Citations (6)*help

See more

References(23)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top