中圖分類號： TP305；TP181
文獻標識碼： A
DOI： 10.19358/j.issn.2097-1788.2022.04.010
引用格式： 張永偉，朱祁，吳永城. 基于分解策略的多標簽在線特征選擇算法[J].網絡安全與數據治理，2022，41(4)：65-71，77.

Online multi-label feature selection algorithm based on binary relevance strategy

Abstract： Online learning method is an efficient and extensible machine learning algorithm for large-scale applications. However, when selecting features for multi-label datasets, the traditional online multi-label learning algorithms need to access all the features of the dataset. When the dataset has a higher dimension, this online learning method cannot be applied to the actual situation. Based on the existing research, a multi-label online feature selection algorithm based on decomposition strategy is proposed by using binary relevance strategy. This algorithm uses sparse regularization and interception methods for online feature selection to reduce computational complexity. The experimental results show that the feature selection performance of the algorithm is better than that of other multi-label online feature selection algorithms.

Key words : feature selection；online learning；multi-label classification；binary relevance strategy