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Balanced Neighborhood Classifiers for Imbalanced Data Sets
Shunzhi ZHU Ying MA Weiwei PAN Xiatian ZHU Guangchun LUO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2014/12/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Pattern Recognition
machine learning, class imbalance, class distribution, classification,
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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.