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

TL;DR

This paper introduces a novel complex network building methodology based on attribute-attribute interactions for high-level classification, which captures hidden attribute patterns without normalization, potentially enhancing existing classification techniques.

Contribution

The proposed methodology advances high-level classification by effectively modeling attribute interactions without normalization, addressing limitations of current kNN-based graph construction methods.

Findings

Method captures hidden attribute patterns

Improves accuracy of high-level classification techniques

Does not require data normalization

Abstract

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.