Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Max-Decision Boundary
LIN Yaojin1,2, CHEN Xiangyan1,2, BAI Shengxing1,2, WANG Chenxi1,2
1. School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000 2. Key Laboratory of Data Science and Intelligence Application, The Education Department of Fujian Province, Minnan Normal University, Zhangzhou 363000
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.
林耀进, 陈祥焰, 白盛兴, 王晨曦. 基于最大决策边界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, 2020, 33(9): 820-829.
LIN Yaojin, CHEN Xiangyan, BAI Shengxing, WANG Chenxi. Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Max-Decision Boundary. , 2020, 33(9): 820-829.
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2020, Vol. 33 
