IEICE TRANSACTIONS on Information
Balanced Neighborhood Classifiers for Imbalanced Data Sets
Summary :
A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.12 pp.3226-3229
- Publication Date
- 2014/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8064
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Shunzhi ZHU
Xiamen University of Technology
Ying MA
Xiamen University of Technology
Weiwei PAN
Xiamen University of Technology
Xiatian ZHU
Queen Mary University of London
Guangchun LUO
University of Electronic Science and Technology of China
Keyword
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Shunzhi ZHU, Ying MA, Weiwei PAN, Xiatian ZHU, Guangchun LUO, "Balanced Neighborhood Classifiers for Imbalanced Data Sets" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 12, pp. 3226-3229, December 2014, doi: 10.1587/transinf.2014EDL8064.
Abstract: A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8064/_p
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@ARTICLE{e97-d_12_3226,
author={Shunzhi ZHU, Ying MA, Weiwei PAN, Xiatian ZHU, Guangchun LUO, },
journal={IEICE TRANSACTIONS on Information},
title={Balanced Neighborhood Classifiers for Imbalanced Data Sets},
year={2014},
volume={E97-D},
number={12},
pages={3226-3229},
abstract={A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.},
keywords={},
doi={10.1587/transinf.2014EDL8064},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Balanced Neighborhood Classifiers for Imbalanced Data Sets
T2 - IEICE TRANSACTIONS on Information
SP - 3226
EP - 3229
AU - Shunzhi ZHU
AU - Ying MA
AU - Weiwei PAN
AU - Xiatian ZHU
AU - Guangchun LUO
PY - 2014
DO - 10.1587/transinf.2014EDL8064
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2014
AB - A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.
ER -
English
