论文标题
通过建模属性 - 属性交互，一种新的网络基础高级数据分类方法（quipus）
A new network-base high-level data classification methodology (Quipus) by modeling attribute-attribute interactions
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，核心是一种复杂的网络构建方法。当前的方法使用KNN的变化来产生这些图。但是，这些技术忽略了属性之间的一些隐藏模式，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。当前的结果表明，这种方法基于中间性，提高了高级分类算法的准确性。
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 a complex network building methodology. The current methodologies use variations of kNN to produce these graphs. However, these techniques ignore some hidden patterns 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. The current results show us that this approach improves the accuracy of the high-level classification algorithm based on betweenness centrality.
