基于邻域粗糙集的高维类不平衡数据在线流特征选择

doi:10.16451/j.cnki.issn1003-6059.201908006

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摘要

在许多实际应用中,数据经常呈现高维不平衡特征,特征还根据需求在不同时间段动态生成.基于此种情况,文中提出基于邻域粗糙集的高维类不平衡数据的在线流特征选择算法.算法设计基于小类重要度的粗糙依赖度计算公式,同时,提出在线相关性分析、在线冗余度分析、在线重要度分析三种策略,用于选择在大类和小类之间具有高可分离性的特征.在7个高维类不平衡数据集上的实验表明,文中算法可以有效选择一个较好的特征子集,性能较优.

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	陈祥焰
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关键词 ：在线特征选择, 高维不平衡数据, 邻域粗糙集, 粗糙依赖度

Abstract：

In many real world applications, data is dynamically generated at different time periods in addition to high-dimensional imbalanced features. An high-dimensional class-imbalanced online feature selection algorithm based on neighborhood rough set is proposed. The algorithm design is based on rough dependency calculation formula of small class significance. Meanwhile, three evaluation criteria of online relevance analysis, online redundancy analysis and online significance analysis, are presented to select features with high separability between majority and minority classes. Experimental results on seven high-dimensional and class-imbalanced datasets show that the proposed method can effectively select a better feature subset with better performance.

Key words： Online Feature Selection High-Dimensional and Class-Imbalance Data Neighborhood Rough Set Rough Dependence

收稿日期: 2019-03-01

ZTFLH:

TP 18

基金资助:

国家自然科学基金项目(No.61672272)、福建省自然科学基金项目(No.2018J01548,2018J01547)、福建省教育厅科技项目(No.JT180318)资助

通讯作者: 林耀进(通讯作者),博士,教授,主要研究方向为数据挖掘、机器学习.E-mail:zzlinyaojin@163.com.

作者简介: 陈祥焰,硕士研究生,主要研究方向为数据挖掘.E-mail:1053214451@qq.com.王晨曦,硕士,讲师,主要研究方向为数据挖掘.E-mail:wangcx5@sina.com.

引用本文:

陈祥焰, 林耀进, 王晨曦. 基于邻域粗糙集的高维类不平衡数据在线流特征选择[J]. 模式识别与人工智能, 2019, 32(8): 726-735. CHEN Xiangyan, LIN Yaojin, WANG Chenxi. Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Neighborhood Rough Set. , 2019, 32(8): 726-735.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201908006 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2019/V32/I8/726

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