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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Organizational Data Classification Based on the Importance Concept of Complex Networks

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Author(s):
Carneiro, Murillo Guimaraes [1] ; Zhao, Liang [2]
Total Authors: 2
Affiliation:
[1] Univ Fed Uberlandia, Fac Comp, BR-38400902 Uberlandia, MG - Brazil
[2] Univ Sao Paulo, Dept Comp & Math, BR-14040901 Ribeirao Preto - Brazil
Total Affiliations: 2
Document type: Journal article
Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS; v. 29, n. 8, p. 3361-3373, AUG 2018.
Web of Science Citations: 2
Abstract

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)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants
FAPESP's process: 12/07926-3 - Evolutionary algorithms to semantic role labeling
Grantee:Murillo Guimarães Carneiro
Support type: Scholarships in Brazil - Doctorate