A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

TL;DR

This paper introduces a novel high-level data classification method based on complex networks and betweenness centrality, demonstrating competitive results across multiple real datasets.

Contribution

The paper presents a new network-based high-level classification algorithm utilizing betweenness centrality, advancing pattern recognition beyond traditional physical feature analysis.

Findings

Achieved competitive classification accuracy on nine real datasets.

Outperformed several traditional classification models in experiments.

Validated the effectiveness of network-based high-level classification.

Abstract

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called \textit{low-level} classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has a facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as \textit{high-level} classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.