High-level classification algorithms focus on the interactions between instances. These produce a new form to evaluate and classify data. In this process, the core is the complex network building methodology because it determines the metrics to be used for classification. The current methodologies use variations of kNN to produce these graphs. However, this technique ignores some hidden pattern between attributes and require normalization to be accurate. In this paper, we propose a new methodology for network building based on attribute-attribute interactions that do not require normalization and capture the hidden patterns of the attributes. The current results show us that could be used to improve some current high-level techniques.

中文翻译：

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基于属性-属性交互的高级分类复杂网络构建新方法

高级分类算法侧重于实例之间的交互。这些产生了一种新的形式来评估和分类数据。在这个过程中，核心是复杂的网络构建方法，因为它决定了用于分类的指标。当前的方法使用 kNN 的变体来生成这些图。然而，这种技术忽略了属性之间的一些隐藏模式，需要规范化才能准确。在本文中，我们提出了一种基于属性-属性交互的网络构建新方法，该方法不需要规范化并捕获属性的隐藏模式。当前的结果向我们表明，可以用来改进当前的一些高级技术。