Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Max-Decision Boundary

被引：0
作者：
Lin Y. [1 ,2 ]
Chen X. [1 ,2 ]
Bai S. [1 ,2 ]
Wang C. [1 ,2 ]
机构：
[1] School of Computer Science and Engineering, Minnan Normal University, Zhangzhou
[2] Key Laboratory of Data Science and Intelligence Application, The Education Department of Fujian Province, Minnan Normal University, Zhangzhou
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Adaptive Neighborhood; High-Dimensional and Class-Imbalanced Data; Neighborhood Rough Set; Online Feature Selection;
D O I：
10.16451/j.cnki.issn1003-6059.202009006
中图分类号：
学科分类号：
摘要：
The feature space of data changes with time dynamically. The number of features on training data is high-dimensional and fixed, and the label space is imbalanced. Motivated by the above, an online streaming feature selection algorithm for high-dimensional and class-imbalanced data based on max-decision boundary is proposed. An adaptive neighborhood relation is defined with consideration of the effect of boundary samples based on neighborhood rough set, and then a rough dependency calculation formula with respect to max-decision boundary is designed. Meanwhile, three online feature subset evaluation metrics are proposed to select features with great discriminability in majority and minority classes. Experiments on eleven high-dimensional and class-imbalanced datasets indicate that the proposed method achieves better performance than some state-of-the-art online streaming feature selection algorithms. © 2020, Science Press. All right reserved.
引用
收藏
页码：820 / 829
页数：9
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