Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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Breve, Fabricio
Zhao, Liang
Quiles, Marcos [UNIFESP]
Pedrycz, Witold
Liu, Jiming
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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.
Ieee Transactions On Knowledge and Data Engineering. Los Alamitos: Ieee Computer Soc, v. 24, n. 9, p. 1686-1698, 2012.