  Semi-supervised learning methods are usually employed in the classification
of data sets where only a small subset of the data items is labeled. In these
scenarios, label noise is a crucial issue, since the noise may easily spread to
a large portion or even the entire data set, leading to major degradation in
classification accuracy. Therefore, the development of new techniques to reduce
the nasty effects of label noise in semi-supervised learning is a vital issue.
Recently, a graph-based semi-supervised learning approach based on Particle
competition and cooperation was developed. In this model, particles walk in the
graphs constructed from the data sets. Competition takes place among particles
representing different class labels, while the cooperation occurs among
particles with the same label. This paper presents a new particle competition
and cooperation algorithm, specifically designed to increase the robustness to
the presence of label noise, improving its label noise tolerance. Different
from other methods, the proposed one does not require a separate technique to
deal with label noise. It performs classification of unlabeled nodes and
reclassification of the nodes affected by label noise in a unique process.
Computer simulations show the classification accuracy of the proposed method
when applied to some artificial and real-world data sets, in which we introduce
increasing amounts of label noise. The classification accuracy is compared to
those achieved by previous particle competition and cooperation algorithms and
other representative graph-based semi-supervised learning methods using the
same scenarios. Results show the effectiveness of the proposed method.
