Multigranulation Relative Entropy-Based Mixed Attribute Outlier Detection in Neighborhood Systems

被引:25
作者
Yuan, Zhong [1 ]
Chen, Hongmei [1 ]
Li, Tianrui [1 ]
Zhang, Xianyong [2 ]
Sang, Binbin [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Comp & Artificial Intelligence, Chengdu 611756, Peoples R China
[2] Sichuan Normal Univ, Sch Math Sci, Chengdu 610066, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 08期
基金
中国国家自然科学基金;
关键词
Anomaly detection; Rough sets; Entropy; Uncertainty; Clustering algorithms; Numerical models; Information entropy; Mixed attribute; multigranulation; neighborhood rough set theory; outlier detection; relative entropy; ALGORITHMS; REDUCTION;
D O I
10.1109/TSMC.2021.3119119
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Outlier detection is widely used in many fields, such as intrusion detection, credit card fraud detection, medical diagnosis, and so on. Existing outlier detection algorithms are mostly designed for dealing with numeric or categorical attributes. However, data usually exist in the form of mixed attributes in real-world applications. In this article, we propose a novel mixed attribute outlier detection method based on multigranulation relative entropy by employing the neighborhood rough set. First, the neighborhood system is constructed by optimizing the mixed distance metric and the radius of the statistical value. Second, the neighborhood entropy is introduced as an uncertainty measure of data. Furthermore, the three kinds of multigranulation relative entropy-based matrices are defined by three kinds of attribute sequences, and the multigranulation relative entropy-based outlier factor is integrated to indicate the outlier degree of every object. Based on the proposed outlier detection model, the corresponding algorithm is designed. Finally, the proposed algorithm is compared with other nine algorithms through experiments on public data. The experimental results show that the proposed technique is adaptive and effective.
引用
收藏
页码:5175 / 5187
页数:13
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