Particle swarm optimization for network-based data classification
Carneiro MG, Cheng R, Zhao L, Jin Y (2019)
Neural Networks 110: 243-255.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Carneiro, Murillo G.;
Cheng, Ran;
Zhao, Liang;
Jin, YaochuUniBi ![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Abstract / Bemerkung
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.
Erscheinungsjahr
2019
Zeitschriftentitel
Neural Networks
Band
110
Seite(n)
243-255
ISSN
0893-6080
Page URI
https://pub.uni-bielefeld.de/record/2978431
Zitieren
Carneiro MG, Cheng R, Zhao L, Jin Y. Particle swarm optimization for network-based data classification. Neural Networks. 2019;110:243-255.
Carneiro, M. G., Cheng, R., Zhao, L., & Jin, Y. (2019). Particle swarm optimization for network-based data classification. Neural Networks, 110, 243-255. https://doi.org/10.1016/j.neunet.2018.12.003
Carneiro, Murillo G., Cheng, Ran, Zhao, Liang, and Jin, Yaochu. 2019. “Particle swarm optimization for network-based data classification”. Neural Networks 110: 243-255.
Carneiro, M. G., Cheng, R., Zhao, L., and Jin, Y. (2019). Particle swarm optimization for network-based data classification. Neural Networks 110, 243-255.
Carneiro, M.G., et al., 2019. Particle swarm optimization for network-based data classification. Neural Networks, 110, p 243-255.
M.G. Carneiro, et al., “Particle swarm optimization for network-based data classification”, Neural Networks, vol. 110, 2019, pp. 243-255.
Carneiro, M.G., Cheng, R., Zhao, L., Jin, Y.: Particle swarm optimization for network-based data classification. Neural Networks. 110, 243-255 (2019).
Carneiro, Murillo G., Cheng, Ran, Zhao, Liang, and Jin, Yaochu. “Particle swarm optimization for network-based data classification”. Neural Networks 110 (2019): 243-255.
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