JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 90-99.doi: 10.6040/j.issn.1671-9352.7.2023.148

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Multi-label online stream feature selection based on high-dimensional correlation

ZHU Liquan1,2, LIN Yaojin1,2, MAO Yu1,2, CHENG Yuxuan1,2   

  1. 1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, Fujian, China;
    2. Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, Fujian, China
  • Published:2024-05-09

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Abstract: This paper proposes a multi-label online stream feature selection algorithm based on high-dimensional correlation. The algorithm employs an equivalent mapping of the label space and constructs a weighted undirected graph based on the high-dimensional label space. It utilizes graph information and Jaccard index to measure the high-dimensional weights between labels. The significance of newly arrived features is calculated based on the high-dimensional correlation of the labels, and the significance level of new features is determined through iterative mean significance. Furthermore, a balanced global and local online feature selection algorithm is designed to dynamically optimize the selected feature subset by considering the global correlation between the selected features and the label space, thereby filtering out irrelevant features. Redundant features are eliminated by analyzing the local correlation among the selected features. The testing results validate the effectiveness of the proposed algorithm through comparative tests with six other multi-label feature selection methods.

Key words: multi-label feature selection, online streaming feature, high dimensional correlation, label weight

CLC Number: 

  • TP391
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