不均衡数据分类算法的综述
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国家自然科学基金(61074076);中国博士后科学基金(20090450119);中国博士点新教师基金(20092304120017)


Overview of classification algorithms for unbalanced data
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The Natural Science Foundation of China(61074076); The China Postdoctoral Science Foundation(20090405119);The China Doctoral New Teachers Foundation(2009230412007)

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    摘要:

    传统的分类方法都是建立在类分布大致平衡这一假设基础上的,然而实际情况中,数据往往都是不均衡的。因此,传统分类器分类性能通常比较有限。从数据层面和算法层面对国内外分类算法做了详细而系统的概述。并通过仿真实验,比较了多种不平衡分类算法在6个不同数据集上的分类性能,发现改进的分类算法在整体性能上得到不同程度的提高,最后列出了不均衡数据分类发展还需解决的一些问题。

    Abstract:

    Traditional classification methods are based on the assumption that the training sets are well-balanced, however, in real case the data is usually unbalanced, and the classification performance of the traditional classification is always restricted. A detailed overview of domestic and foreign classification algorithms from the data level and algorithm level is provided in this paper. And through simulation experiments to compare the classification performance of a variety of unbalanced classification algorithm on six different data sets, it is found that the improved classification algorithm has varying degrees of improvement for overall performance. The paper concludes with a list of problems which need solving for the development of unbalanced data classification.

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陶新民,郝思媛,张冬雪,徐鹏.不均衡数据分类算法的综述[J].重庆邮电大学学报(自然科学版),2013,25(1):101-110.

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  • 收稿日期:2012-06-07
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  • 在线发布日期: 2013-02-26

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