Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Organizational Data Classification Based on the Importance Concept of Complex Networks

Texto completo
Autor(es):
Carneiro, Murillo Guimaraes [1] ; Zhao, Liang [2]
Número total de Autores: 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
Número total de Afiliações: 2
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:José Alberto Cuminato
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
Beneficiário:Elbert Einstein Nehrer Macau
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 12/07926-3 - Algoritmos evolutivos para anotação de papéis semânticos
Beneficiário:Murillo Guimarães Carneiro
Linha de fomento: Bolsas no Brasil - Doutorado