Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

• Autor(a) principal: Breve, Fabricio [UNESP]
• Data de Publicação: 2012
• Outros Autores: Zhao, Liang
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/SBRN.2012.16; http://hdl.handle.net/11449/73831
• topic: Computational intelligence; Machine learning; Co-operative behaviors; Competition and cooperation; Critical points; Data items; Data sets; Different sizes; Graph-based; Input datas; Label propagation; Mislabeled data; Network-based; Node degree; Numerical comparison; Prevent error propagation; Real world data; Semi-supervised learning; Semi-supervised learning methods; Artificial intelligence; Behavioral research; Graphic methods; Learning systems; Neural networks; Numerical methods; Virtual reality; Supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.