Balanced Neighborhood Classifiers for Imbalanced Data Sets

被引：0

作者：

Zhu, Shunzhi ^{[1
]}

Ma, Ying ^{[1
]}

Pan, Weiwei ^{[1
]}

Zhu, Xiatian ^{[2
]}

Luo, Guangchun ^{[3
]}

机构：

[1] Xiamen Univ Technol, Xiamen, Peoples R China

[2] Queen Mary Univ London, London E1 4NS, England

[3] Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2014年 / E97D卷 / 12期

基金：

中国国家自然科学基金;

关键词：

machine learning; class imbalance; class distribution; classification; ALGORITHMS;

D O I：

10.1587/transinf.2014EDL8064

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3226 / 3229

页数：4

共 50 条

[41] Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data
Brygala, Magdalena
RISKS, 2022, 10 (02)
[42] Large margin classifiers to generate synthetic data for imbalanced datasets
Ladeira Marques, Marcelo
Moraes Villela, Saulo
Hasenclever Borges, Carlos Cristiano
APPLIED INTELLIGENCE, 2020, 50 (11) : 3678 - 3694
[43] Random Balance: Ensembles of variable priors classifiers for imbalanced data
Diez-Pastor, Jose F.
Rodriguez, Juan J.
Garcia-Osorio, Cesar
Kuncheva, Ludmila I.
KNOWLEDGE-BASED SYSTEMS, 2015, 85 : 96 - 111
[44] Class-imbalanced classifiers for high-dimensional data
Lin, Wei-Jiun
Chen, James J.
BRIEFINGS IN BIOINFORMATICS, 2013, 14 (01) : 13 - 26
[45] Imbalanced data issues in machine learning classifiers: a case study
Gong, Mingxing
JOURNAL OF OPERATIONAL RISK, 2022, 17 (04): : 17 - 36
[46] Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams
Korycki, Lukasz
Cano, Alberto
Krawczyk, Bartosz
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 2334 - 2343
[47] Large margin classifiers to generate synthetic data for imbalanced datasets
Marcelo Ladeira Marques
Saulo Moraes Villela
Carlos Cristiano Hasenclever Borges
Applied Intelligence, 2020, 50 : 3678 - 3694
[48] Neighborhood classifiers
Hu, Qinghua
Yu, Daren
Me, Zongxia
EXPERT SYSTEMS WITH APPLICATIONS, 2008, 34 (02) : 866 - 876
[49] Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data
Fernandes, Everlandio R. Q.
de Carvalho, Andre C. P. L. F.
Yao, Xin
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 32 (06) : 1104 - 1115
[50] Correlations of random classifiers on large data sets
Kurkova, Vera
Sanguineti, Marcello
SOFT COMPUTING, 2021, 25 (19) : 12641 - 12648

← 1 2 3 4 5 →