Feature Selection via 
1-Penalized Squared-Loss Mutual Information
Summary :
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.7 pp.1513-1524
- Publication Date
- 2013/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.1513
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Wittawat JITKRITTUM
Tokyo Institute of Technology
Hirotaka HACHIYA
Tokyo Institute of Technology
Masashi SUGIYAMA
Tokyo Institute of Technology
Keyword
Latest Issue
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Wittawat JITKRITTUM, Hirotaka HACHIYA, Masashi SUGIYAMA, "Feature Selection via
Abstract: Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1513/_p
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@ARTICLE{e96-d_7_1513,
author={Wittawat JITKRITTUM, Hirotaka HACHIYA, Masashi SUGIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Feature Selection via
year={2013},
volume={E96-D},
number={7},
pages={1513-1524},
abstract={Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose
keywords={},
doi={10.1587/transinf.E96.D.1513},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Feature Selection via
T2 - IEICE TRANSACTIONS on Information
SP - 1513
EP - 1524
AU - Wittawat JITKRITTUM
AU - Hirotaka HACHIYA
AU - Masashi SUGIYAMA
PY - 2013
DO - 10.1587/transinf.E96.D.1513
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2013
AB - Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose
ER -
English
