IEICE TRANSACTIONS on Information
Feature Subset Selection for Ordered Logit Model via Tangent-Plane-Based Approximation
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.5 pp.1046-1053
- Publication Date
- 2019/05/01
- Publicized
- 2019/02/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7188
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Mizuho NAGANUMA
The University of Electro-Communications
Yuichi TAKANO
University of Tsukuba
Ryuhei MIYASHIRO
Tokyo University of Agriculture and Technology
Keyword
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Mizuho NAGANUMA, Yuichi TAKANO, Ryuhei MIYASHIRO, "Feature Subset Selection for Ordered Logit Model via Tangent-Plane-Based Approximation" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 5, pp. 1046-1053, May 2019, doi: 10.1587/transinf.2018EDP7188.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7188/_p
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@ARTICLE{e102-d_5_1046,
author={Mizuho NAGANUMA, Yuichi TAKANO, Ryuhei MIYASHIRO, },
journal={IEICE TRANSACTIONS on Information},
title={Feature Subset Selection for Ordered Logit Model via Tangent-Plane-Based Approximation},
year={2019},
volume={E102-D},
number={5},
pages={1046-1053},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7188},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Feature Subset Selection for Ordered Logit Model via Tangent-Plane-Based Approximation
T2 - IEICE TRANSACTIONS on Information
SP - 1046
EP - 1053
AU - Mizuho NAGANUMA
AU - Yuichi TAKANO
AU - Ryuhei MIYASHIRO
PY - 2019
DO - 10.1587/transinf.2018EDP7188
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2019
AB - 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.
ER -
English
