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(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.)

Particle swarm optimization for network-based data classification

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Autor(es):
Carneiro, Murillo G. [1] ; Cheng, Ran [2] ; Zhao, Liang [3] ; Jin, Yaochu [4]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Fed Uberlandia, Fac Comp, BR-38400902 Uberlandia, MG - Brazil
[2] Southern Univ Sci & Technol, Shenzhen Key Lab Computat Intelligence, Univ Key Lab Evolving Intelligent Syst Guangdong, Dept Comp Sci & Engn, Shenzhen 518055 - Peoples R China
[3] Univ Sao Paulo, Dept Comp & Math, BR-14040901 Ribeirao Preto, SP - Brazil
[4] Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey - England
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: NEURAL NETWORKS; v. 110, p. 243-255, FEB 2019.
Citações Web of Science: 0
Resumo

Complex networks provide a powerful tool for data representation due to its ability to describe the interplay between topological, functional, and dynamical properties of the input data. A fundamental process in network-based (graph-based) data analysis techniques is the network construction from original data usually in vector form. Here, a natural question is: How to construct an ``optimal'' network regarding a given processing goal? This paper investigates structural optimization in the context of network-based data classification tasks. To be specific, we propose a particle swarm optimization framework which is responsible for building a network from vector-based data set while optimizing a quality function driven by the classification accuracy. The classification process considers both topological and physical features of the training and test data and employing PageRank measure for classification according to the importance concept of a test instance to each class. Results on artificial and real-world problems reveal that data network generated using structural optimization provides better results in general than those generated by classical network formation methods. Moreover, this investigation suggests that other kinds of network-based machine learning and data mining tasks, such as dimensionality reduction and data clustering, can benefit from the proposed structural optimization method. (C) 2018 Elsevier Ltd. All rights reserved. (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