Multi-label learning has emerged as a crucial paradigm in data analysis, addressing scenarios where instances are associated with multiple class labels simultaneously. With the growing prevalence of multi-label data across diverse applications, such as text and image classification, the significance of multi-label feature selection has become increasingly evident. This paper presents a novel information-theoretical filter-based multi-label feature selection, called ATR, with a new heuristic function. Incorporating a combinations of algorithm adaptation and problem transformation approaches, ATR ranks features considering individual labels as well as abstract label space discriminative powers. Our experimental studies encompass twelve benchmarks spanning various domains, demonstrating the superiority of our approach over ten state-of-the-art information-theoretical filter-based multi-label feature selection methods across six evaluation metrics. Furthermore, our experiments affirm the scalability of ATR for benchmarks characterized by extensive feature and label spaces. The codes are available at https://github.com/Sadegh28/ATR

该论文介绍了一种基于信息论的多标签特征选择方法ATR，通过算法适应性和问题转换方法的组合，能够根据个别标签和抽象标签空间的区分能力对特征进行排名，并且在六个评估指标上优于其他十种信息论滤波器的多标签特征选择方法。

自适应和转换相关性的多标签特征选择