Information theory has emerged as a prominent approach for analyzing feature relevance and redundancy in multi-label feature selection. However, traditional information theory-based methods encounter two primary issues. Firstly, when evaluating feature relevance, they fail to consider the differing importance of each label within the entire label set. Secondly, when assessing feature redundancy, they overlook the varying dependencies of the selected features on the labels. To address these issues, this paper proposes a novel multi-label feature selection method that considers label importance-weighted relevance and label-dependency redundancy. Specifically, we introduce the concept of label importance weight (LIW) to measure the significance of each label within the entire label set. Based on this LIW, we define a feature relevance term called label importance-weighted relevance (LIWR). Subsequently, we leverage the uncertainty coefficient to quantify the dependence of the selected features on the labels, treating it as a weight. Building upon this weight, we establish a feature redundancy term known as label-dependency redundancy (LDR). Finally, we formulate a feature evaluation criterion called LIWR-LDR by maximizing LIWR and minimizing LDR, accompanied by the presentation of a corresponding feature selection algorithm. Extensive experiments conducted on 25 multi-label datasets demonstrate the effectiveness of LIWR-LDR.

中文翻译：

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考虑标签重要性加权相关性和标签依赖冗余的多标签特征选择


信息论已成为分析多标签特征选择中特征相关性和冗余性的重要方法。然而，传统的基于信息论的方法遇到了两个主要问题。首先，在评估特征相关性时，他们没有考虑整个标签集中每个标签的不同重要性。其次，在评估特征冗余时，它们会忽略所选特征对标签的不同依赖关系。为了解决这些问题，本文提出了一种新的多标签特征选择方法，该方法考虑了标签重要性加权相关性和标签依赖性冗余。具体来说，我们引入了标签重要性权重 （LIW） 的概念来衡量整个标签集中每个标签的显著性。基于这个 LIW，我们定义了一个称为标签重要性加权相关性 （LIWR） 的特征相关性术语。随后，我们利用不确定性系数来量化所选特征对标签的依赖性，将其视为权重。基于此权重，我们建立了一个称为标签依赖性冗余 （LDR） 的特征冗余术语。最后，我们通过最大化 LIWR 和最小化 LDR 来制定一个名为 LIWR-LDR 的特征评估标准，并同时提出相应的特征选择算法。在 25 个多标签数据集上进行的广泛实验证明了 LIWR-LDR 的有效性。