Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise

• URL: http://arxiv.org/abs/2002.05198v1
• Date: Wed, 12 Feb 2020 19:44:59 GMT
• Title: Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise
• Authors: Fabricio Aparecido Breve, Liang Zhao, Marcos Gon\c{c}alves Quiles
• Abstract summary: A graph-based semi-supervised learning approach based on Particle competition and cooperation was developed. This paper presents a new particle competition and cooperation algorithm, specifically designed to increase the robustness to the presence of label noise. It performs classification of unlabeled nodes and reclassification of the nodes affected by label noise in a unique process.
• Score: 6.247917165799351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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