New complex network building methodology for High Level Classification based on attribute-attribute interaction

High-level classification algorithms focus on the interactions between\ninstances. These produce a new form to evaluate and classify data. In this\nprocess, the core is the complex network building methodology because it\ndetermines the metrics to be used for classification. The current methodologies\nuse variations of kNN to produce these graphs. However, this technique ignores\nsome hidden pattern between attributes and require normalization to be\naccurate. In this paper, we propose a new methodology for network building\nbased on attribute-attribute interactions that do not require normalization and\ncapture the hidden patterns of the attributes. The current results show us that\ncould be used to improve some current high-level techniques.\n