抽样技术和CBES分类非平衡数据集

计算机科学 ›› 2013, Vol. 40 ›› Issue (12): 70-74.

抽样技术和CBES分类非平衡数据集

职为梅,郭华平,范明

郑州大学信息工程学院郑州450052;郑州大学信息工程学院郑州450052;郑州大学信息工程学院郑州450052

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金(60773048)资助

Sampling Techniques with CBES for Imbalanced Learning

ZHI Wei-mei,GUO Hua-ping and FAN Ming

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： CBES是面向非平衡数据集分类的组合选择方法。相关的实验表明,CBES方法能大幅度提升基分类器的泛化能力。已有研究表明,抽样方法能有效提高分类器在非平衡数据集分类上的性能。因此,巧妙地将抽样技术应用到CBES方法中,进而提出基于抽样的CBES方法(SCBES),以期进一步提高CBES在稀有类上的性能。大量的实验表明,巧妙地使用抽样方法能进一步提高CBES方法在非平衡数据集分类上的性能。

关键词: 非平衡数据集,组合分类器,组合选择,抽样技术

Abstract: CBES is a method which can be used for classification of imbalanced datasets．Related experimental results show CBES can boost the generalization ability of the base classifier．Reported researches show sampling method can effectively improve the performance of rare data．In the paper,we skillfully used sampling methods into CBES,and then proposed a method,named sampling-based CBES (SCBES) to further improve the classification performance of rare data．The experimental results demonstrate SCBES can effectively improve the performance of classification for imbalanced datasets．

Key words: Imbalanced data sets,Ensemble,Ensemble selection,Sampling method

职为梅,郭华平,范明. 抽样技术和CBES分类非平衡数据集[J]. 计算机科学, 2013, 40(12): 70-74. https://doi.org/

ZHI Wei-mei,GUO Hua-ping and FAN Ming. Sampling Techniques with CBES for Imbalanced Learning[J]. Computer Science, 2013, 40(12): 70-74. https://doi.org/

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