Organizational Data Classification Based on the Importance Concept of Complex Networks

Autor(es)

Carneiro, Murillo Guimaraes ^[1] ; Zhao, Liang ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Uberlandia, Fac Comp, BR-38400902 Uberlandia, MG - Brazil

^[2] Univ Sao Paulo, Dept Comp & Math, BR-14040901 Ribeirao Preto - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS; v. 29, n. 8, p. 3361-3373, AUG 2018.
• Citações Web of Science: 2

Resumo

Data classification is a common task, which can be performed by both computers and human beings. However, a fundamental difference between them can be observed: computer-based classification considers only physical features (e.g., similarity, distance, or distribution) of input data; by contrast, brain-based classification takes into account not only physical features, but also the organizational structure of data. In this paper, we figure out the data organizational structure for classification using complex networks constructed from training data. Specifically, an unlabeled instance is classified by the importance concept characterized by Google's PageRank measure of the underlying data networks. Before a test data instance is classified, a network is constructed from vector-based data set and the test instance is inserted into the network in a proper manner. To this end, we also propose a measure, called spatio-structural differential efficiency, to combine the physical and topological features of the input data. Such a method allows for the classification technique to capture a variety of data patterns using the unique importance measure. Extensive experiments demonstrate that the proposed technique has promising predictive performance on the detection of heart abnormalities. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 12/07926-3 - Algoritmos Evolutivos para Anotação de Papéis Semânticos
• Beneficiário: Murillo Guimarães Carneiro
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
• Beneficiário: Elbert Einstein Nehrer Macau
• Modalidade de apoio: Auxílio à Pesquisa - Temático