Abstract Multi-label learning deals with cases where every instance corresponds to multiple labels. The objective is to learn mapping from an instance to a relevant label set. Existing multi-label learning approaches assume that the significance for all related labels is same for every instance. Several problems of label ambiguity can be dealt with using multi-label learning, but some practical applications with significance among related labels for every instance cannot be effectively processed. To achieve superior results by conducting different significance of labels, label distribution learning is used for such applications. First, the probability model and rough set are embedded in the labeling significance, thus more supervised information can be obtained from original multi-label data. Subsequently, to resolve the feature selection problem of label distribution data, according to the feature dependency and the rough set, a novel feature selection algorithm for multi-label classification is designed. Finally, to verify the effectiveness of the proposed algorithms, an extensive experiment is conducted on 15 real-world multiple label data sets. The performance of the proposed algorithm through the multi-label classifier is compared with seven state-of-the-art approaches, thereby indicating the applicability and effectiveness of label distribution feature selection.

中文翻译：

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粗糙集多标签分类的标签分布特征选择

摘要 多标签学习处理每个实例对应多个标签的情况。目标是学习从实例到相关标签集的映射。现有的多标签学习方法假设所有相关标签的重要性对于每个实例都是相同的。使用多标签学习可以解决标签歧义的几个问题，但无法有效处理一些在每个实例的相关标签之间具有重要意义的实际应用。为了通过对标签进行不同意义的处理来获得更好的结果，标签分布学习用于此类应用。首先，概率模型和粗糙集被嵌入到标签的重要性中，从而可以从原始的多标签数据中获得更多的监督信息。随后，针对标签分布数据的特征选择问题，根据特征依赖和粗糙集，设计了一种新的多标签分类特征选择算法。最后，为了验证所提出算法的有效性，在 15 个真实世界的多标签数据集上进行了广泛的实验。通过多标签分类器将所提出算法的性能与七种最先进的方法进行比较，从而表明标签分布特征选择的适用性和有效性。对 15 个真实世界的多标签数据集进行了广泛的实验。通过多标签分类器将所提出算法的性能与七种最先进的方法进行比较，从而表明标签分布特征选择的适用性和有效性。对 15 个真实世界的多标签数据集进行了广泛的实验。通过多标签分类器将所提出算法的性能与七种最先进的方法进行比较，从而表明标签分布特征选择的适用性和有效性。