New complex network building methodology for High Level Classification based on attribute-attribute interaction
This work addresses a domain-specific bottleneck in high-level classification algorithms by offering an incremental improvement over current kNN-based methods.
The paper tackles the problem of building complex networks for high-level classification by introducing a new methodology based on attribute-attribute interactions, which eliminates the need for normalization and captures hidden patterns, potentially improving existing techniques.
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.