基于最大决策边界的高维类不平衡数据在线流特征选择

doi:10.16451/j.cnki.issn1003-6059.202009006

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Abstract 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.

Key words： Online Feature Selection High-Dimensional and Class-Imbalanced Data Adaptive Neighborhood Neighborhood Rough Set

Received: 01 July 2020

ZTFLH:

TP 18

Fund:National Natural Science Foundation of China(No.61672272), Natural Science Foundation of Fujian Province(No.2018J01548,2018J01547), Science and Technology Project of the Education Department of Fujian Province(No.JAT180318)

Corresponding Authors: LIN Yaojin, Ph.D., professor. His research interests include data mining and machine learning.

About author:: CHEN Xiangyan, master student. His research interests include data mining.BAI Shengxing, master student. His research interests include data mining.WANG Chenxi, master, lecturer. Her research interests include data mining.

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Cite this article:

LIN Yaojin,CHEN Xiangyan,BAI Shengxing等. Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Max-Decision Boundary[J]. , 2020, 33(9): 820-829.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202009006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I9/820

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