A new network-base high-level data classification methodology (Quipus) by modeling attribute-attribute interactions

TL;DR

This paper introduces Quipus, a novel network-based high-level data classification method that models attribute-attribute interactions without normalization, improving accuracy over existing kNN-based approaches.

Contribution

The paper presents a new network construction methodology based on attribute interactions that enhances classification accuracy without normalization requirements.

Findings

Improved classification accuracy using the proposed network model.

Elimination of normalization step in network construction.

Enhanced detection of hidden attribute patterns.

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 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.