The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article concerne une approche d'optimisation d'entiers mixtes (MIO) pour sélectionner un sous-ensemble de fonctionnalités pertinentes parmi de nombreux candidats. Pour la classification ordinale, un modèle logit séquentiel et un modèle logit ordonné sont souvent utilisés. Pour la sélection de sous-ensembles de fonctionnalités dans le modèle logit séquentiel, Sato et al.[22] a récemment proposé une formulation d'optimisation linéaire en nombres entiers mixtes (MILO). Dans leur formulation MILO, une fonction non linéaire univariée contenue dans le modèle logit séquentiel était représentée par une approximation basée sur la tangente. Nous étendons cette formulation MILO au modèle logit ordonné, qui est plus couramment utilisé pour la classification ordinale que le modèle logit séquentiel. En utilisant des plans tangents pour approximer une fonction non linéaire bivariée impliquée dans le modèle logit ordonné, nous dérivons une formulation MILO pour la sélection de sous-ensembles de caractéristiques dans le modèle logit ordonné. Nos résultats informatiques vérifient que la méthode proposée est supérieure au modèle logit ordonné régularisé L1 en termes de qualité de solution.
Mizuho NAGANUMA
The University of Electro-Communications
Yuichi TAKANO
University of Tsukuba
Ryuhei MIYASHIRO
Tokyo University of Agriculture and Technology
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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},}
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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 -