Particle Competition and Cooperation in Networks for Semi-Supervised Learning
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Data
2012-09-01
Autores
Breve, Fabricio Aparecido [UNESP]
Zhao, Liang
Quiles, Marcos
Pedrycz, Witold
Liu, Jiming
Título da Revista
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Título de Volume
Editor
Institute of Electrical and Electronics Engineers (IEEE), Computer Soc
Resumo
Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a divide-and-conquer effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.
Descrição
Palavras-chave
Semi-supervised learning, particles competition and cooperation, network-based methods, label propagation
Como citar
IEEE Transactions on Knowledge and Data Engineering. Los Alamitos: IEEE Computer Soc, v. 24, n. 9, p. 1686-1698, 2012.