论文标题
基于属性 - 属性互动的高级分类的新的复杂网络构建方法
New complex network building methodology for High Level Classification based on attribute-attribute interaction
论文作者
论文摘要
高级分类算法集中于实例之间的相互作用。这些产生了一种新表格来评估和分类数据。在此过程中，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的变化来产生这些图。但是，该技术忽略了属性之间的某些隐藏模式，要求归一化必须准确。在本文中，我们提出了一种基于属性 - 属性交互的网络构建方法，该方法不需要标准化并捕获属性的隐藏模式。当前的结果表明我们可以用来改善一些当前的高级技术。
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.
