Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (15): 52-67.DOI: 10.3778/j.issn.1002-8331.2202-0114

• Research Hotspots and Reviews • Previous Articles     Next Articles

Research Progress of Multi-Label Feature Selection

ZHOU Huiying, WANG Tinghua, ZHANG Daili   

  1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, Jiangxi 341000, China
  • Online:2022-08-01 Published:2022-08-01



  1. 赣南师范大学 数学与计算机科学学院,江西 赣州 341000

Abstract: Feature selection has always been an important issue in machine learning and data mining. In multi-label learning tasks, each sample in the multi-label dataset is associated with multiple labels and different labels are also usually related. In multi-label high-dimensional data analysis, multi-label feature selection methods are proposed to reduce feature dimension and improve classification performance. This paper reviews the research progress of multi-label feature selection. After introducing multi-label classification and evaluation criteria, three kinds of multi-label feature selection approaches are analyzed in detail, namely, filter, wrapper, and embedded algorithms. Finally, the future research of multi-label feature selection is prospected.

Key words: feature selection,  , multi-label classification,  , machine learning, data mining

摘要: 特征选择一直是机器学习和数据挖掘中的一个重要问题。在多标签学习任务中,数据集中的每个样本都与多个标签相关联,标签与标签之间通常也是相关的。在多标签高维数据分析中,为降低特征维数和提高分类性能,研究者们提出了多标签特征选择方法。系统综述了多标签特征选择的研究进展。在介绍多标签分类以及评价准则之后,详细分析了多标签特征选择的三类方法,即过滤式算法、包裹式算法和嵌入式算法,对多标签特征选择未来的研究提出展望。

关键词: 特征选择, 多标签分类, 机器学习, 数据挖掘