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

  • Esteban Zuñiga Universidade de São Paulo
  • Liang Zhao Universidade de São Paulo


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 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 pat-terns 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 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.

Palavras-chave: Machine Learning, Data Mining, Data Science


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ZUÑIGA, Esteban; ZHAO, Liang. A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 188-198. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2020.12128.