Most multi-label learning (MLL) techniques perform classification by analyzing only the physical features of the data, which means they are unable to consider high-level features, such as structural and topological ones. Consequently, they have trouble to detect the semantic meaning of the data (e.g., formation pattern). To handle this problem, a high-level framework has been recently proposed to the MLL task, in which the high-level features are extracted using the analysis of complex network measures. In this paper, we extend that work by evaluating different combinations of four complex networks measures, namely clustering coefficient, assortativity, average degree and average path length. Experiments conducted over seven real-world data sets showed that the low-level techniques often can have their predictive performance improved after being combined with high-level ones, and also demonstrated that there is no a unique measure that provides the best results, i.e., different problems may ask for different network properties in order to have their high-level patterns efficiently detected.

中文翻译：

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多标签分类的复杂网络度量分析

大多数多标签学习 (MLL) 技术仅通过分析数据的物理特征来执行分类，这意味着它们无法考虑高级特征，例如结构和拓扑特征。因此，他们难以检测数据的语义含义（例如，形成模式）。为了解决这个问题，最近为 MLL 任务提出了一个高级框架，其中使用复杂网络度量的分析来提取高级特征。在本文中，我们通过评估四种复杂网络度量的不同组合来扩展这项工作，即聚类系数、分类性、平均度和平均路径长度。