JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 70-81.doi: 10.6040/j.issn.1671-9352.7.2023.4523

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Online multi-label feature selection based on sub-correlation features and neighborhood mutual information

CHENG Yuxuan1,2, MAO Yu1,2*, ZHANG Xiaoqing1,2, ZENG Yixiang1,2, LIN Yaojin1,2   

  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: To fully mine the features neglected by the single metric algorithm but beneficial to the classifier, this paper proposes an online multi-label feature selection algorithm based on sub-correlation features and neighborhood mutual information. By calculating the importance and correlation of newly arrived features, the difference between the significance of new features is analyzed, and the features are divided into salient features and sub-correlation features. Redundancy analysis is performed on newly arrived features and selected feature sets using neighborhood interaction information, and features with low dependencies are eliminated, to gradually improve the quality of feature subsets. This paper also constructs a measurement index based on the global linear and nonlinear relationship and uses it to calculate the local correlation of features, effectively mining the sub-correlation features. Strip the sub-correlation features from the feature set and save them separately, so that they will not be eliminated from the feature set during the redundancy analysis stage due to the high sensitivity of the salient features to the measurement index. Using established feature selection indicators and iterative strategies to select features according to the indicators. Experimental results show that the proposed algorithm has good effectiveness and stability.

Key words: online feature selection, multi-label learning, neighborhood entropy, neighborhood mutual information, sub-correlation feature

CLC Number: 

  • TP391
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