IEICE TRANSACTIONS on Information
Efficient Reformulation of 1-Norm Ranking SVM
Summary :
Finding linear functions that maximize AUC scores is important in ranking research. A typical approach to the ranking problem is to reduce it to a binary classification problem over a new instance space, consisting of all pairs of positive and negative instances. Specifically, this approach is formulated as hard or soft margin optimization problems over pn pairs of p positive and n negative instances. Solving the optimization problems directly is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p+n. In this paper, we reformulate the ranking problem as variants of hard and soft margin optimization problems over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of AUC scores.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.3 pp.719-729
- Publication Date
- 2018/03/01
- Publicized
- 2017/12/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7233
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Daiki SUEHIRO
Kyushu University,RIKEN
Kohei HATANO
RIKEN,Kyushu University
Eiji TAKIMOTO
Kyushu University
Keyword
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Daiki SUEHIRO, Kohei HATANO, Eiji TAKIMOTO, "Efficient Reformulation of 1-Norm Ranking SVM" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 3, pp. 719-729, March 2018, doi: 10.1587/transinf.2017EDP7233.
Abstract: Finding linear functions that maximize AUC scores is important in ranking research. A typical approach to the ranking problem is to reduce it to a binary classification problem over a new instance space, consisting of all pairs of positive and negative instances. Specifically, this approach is formulated as hard or soft margin optimization problems over pn pairs of p positive and n negative instances. Solving the optimization problems directly is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p+n. In this paper, we reformulate the ranking problem as variants of hard and soft margin optimization problems over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of AUC scores.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7233/_p
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@ARTICLE{e101-d_3_719,
author={Daiki SUEHIRO, Kohei HATANO, Eiji TAKIMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Reformulation of 1-Norm Ranking SVM},
year={2018},
volume={E101-D},
number={3},
pages={719-729},
abstract={Finding linear functions that maximize AUC scores is important in ranking research. A typical approach to the ranking problem is to reduce it to a binary classification problem over a new instance space, consisting of all pairs of positive and negative instances. Specifically, this approach is formulated as hard or soft margin optimization problems over pn pairs of p positive and n negative instances. Solving the optimization problems directly is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p+n. In this paper, we reformulate the ranking problem as variants of hard and soft margin optimization problems over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of AUC scores.},
keywords={},
doi={10.1587/transinf.2017EDP7233},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Efficient Reformulation of 1-Norm Ranking SVM
T2 - IEICE TRANSACTIONS on Information
SP - 719
EP - 729
AU - Daiki SUEHIRO
AU - Kohei HATANO
AU - Eiji TAKIMOTO
PY - 2018
DO - 10.1587/transinf.2017EDP7233
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2018
AB - Finding linear functions that maximize AUC scores is important in ranking research. A typical approach to the ranking problem is to reduce it to a binary classification problem over a new instance space, consisting of all pairs of positive and negative instances. Specifically, this approach is formulated as hard or soft margin optimization problems over pn pairs of p positive and n negative instances. Solving the optimization problems directly is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p+n. In this paper, we reformulate the ranking problem as variants of hard and soft margin optimization problems over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of AUC scores.
ER -
English
