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

• 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

抄録

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.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E102.D (5), 1046-1053, 2019-05-01

  一般社団法人 電子情報通信学会

キーワード

• optimization
• statistics
• feature subset selection
• ordered logit model

詳細情報

• CRID: 1390282763112162944
• NII論文ID: 130007641148
• DOI: 10.1587/transinf.2018edp7188
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E102.D/5/E102.D_2018EDP7188/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可