Balanced Neighborhood Classifiers for Imbalanced Data Sets

Shunzhi ZHU  Ying MA  Weiwei PAN  Xiatian ZHU  Guangchun LUO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.12   pp.3226-3229
Publication Date: 2014/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDL8064
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
machine learning,  class imbalance,  class distribution,  classification,  

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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.