Artículos de revistas
Particle Competition and Cooperation in Networks for Semi-Supervised Learning
Fecha
2012Registro en:
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, LOS ALAMITOS, v. 24, n. 9, pp. 1686-1698, SEP, 2012
1041-4347
10.1109/TKDE.2011.119
Autor
Breve, Fabricio
Liang, Zhao
Quiles, Marcos
Pedrycz, Witold
Liu, Jiming
Institución
Resumen
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.