Issue No. 09 - Sept. (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.119
Fabricio Breve , University of São Paulo, São Carlos
Liang Zhao , University of São Paulo, São Carlos
Marcos Quiles , Federal University of São Paulo (Unifesp), São José dos Campos
Witold Pedrycz , University of Alberta, Edmonton
Jiming Liu , Hong Kong Baptist University, Hong Kong
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.
Supervised learning, Electronic mail, Computational modeling, Unsupervised learning, Machine learning, Labeling, Computational complexity, label propagation, Semi-supervised learning, particles competition and cooperation, network-based methods
M. Quiles, L. Zhao, F. Breve, W. Pedrycz and J. Liu, "Particle Competition and Cooperation in Networks for Semi-Supervised Learning," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1686-1698, 2011.