PIECEWISE-LINEAR APPROXIMATION FOR FEATURE SUBSET SELECTION IN A SEQUENTIAL LOGIT MODEL
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- Sato Toshiki
- University of Tsukuba
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- Takano Yuichi
- Senshu University
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- Miyashiro Ryuhei
- Tokyo University of Agriculture and Technology
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Abstract
<p>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.</p>
Journal
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- Journal of the Operations Research Society of Japan
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Journal of the Operations Research Society of Japan 60 (1), 1-14, 2017
The Operations Research Society of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390001204110230016
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- NII Article ID
- 130005316137
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- NII Book ID
- AA00703935
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- ISSN
- 21888299
- 04534514
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- NDL BIB ID
- 027850659
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed