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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.)

Particle swarm optimization for network-based data classification

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Author(s):
Carneiro, Murillo G. [1] ; Cheng, Ran [2] ; Zhao, Liang [3] ; Jin, Yaochu [4]
Total Authors: 4
Affiliation:
[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
Total Affiliations: 4
Document type: Journal article
Source: NEURAL NETWORKS; v. 110, p. 243-255, FEB 2019.
Web of Science Citations: 2
Abstract

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)

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