Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers
Summary :
Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.8 pp.1871-1874
- Publication Date
- 2013/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.1871
- Type of Manuscript
- LETTER
- Category
- Fundamentals of Information Systems
Authors
Hyunha NAM
Tokyo Institute of Technology
Hirotaka HACHIYA
Tokyo Institute of Technology
Masashi SUGIYAMA
Tokyo Institute of Technology
Keyword
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Hyunha NAM, Hirotaka HACHIYA, Masashi SUGIYAMA, "Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 8, pp. 1871-1874, August 2013, doi: 10.1587/transinf.E96.D.1871.
Abstract: Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1871/_p
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@ARTICLE{e96-d_8_1871,
author={Hyunha NAM, Hirotaka HACHIYA, Masashi SUGIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers},
year={2013},
volume={E96-D},
number={8},
pages={1871-1874},
abstract={Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.},
keywords={},
doi={10.1587/transinf.E96.D.1871},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers
T2 - IEICE TRANSACTIONS on Information
SP - 1871
EP - 1874
AU - Hyunha NAM
AU - Hirotaka HACHIYA
AU - Masashi SUGIYAMA
PY - 2013
DO - 10.1587/transinf.E96.D.1871
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2013
AB - Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.
ER -
English
