%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/08.15.22.59
%2 sid.inpe.br/sibgrapi/2016/08.15.22.59.08
%T Network Construction and Applications for Semi-Supervised Learning
%D 2016
%A Berton, Lilian,
%A Lopes, Alneu de Andrade,
%@affiliation Universidade do Estado de Santa Catarina
%@affiliation Universidade de São Paulo
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K network construction, graph-based methods, semi-supervised learning, complex networks.
%X The influence of network construction on graph-based semi-supervised learning (SSL) and their related applications have only received limited study despite its critical impact on accuracy. We introduce four variants for networkconstruction for SSL that adopt different network topology: 1) S-kNN (Sequential k-Nearest Neighbors) that generates regular networks; 2) GBILI (Graph Based on the informativeness of Labeled Instances) and 3) RGCLI (Robust Graph that Considers Labeled Instances), which exploit the labels available generating scale-free networks; 4) GBLP (Graph Based on Link Prediction), which are based on link prediction measures and creates smallworld networks. Comprehensive experimental results using several benchmark datasets show that it can achieve or outperform existing state-of-the-art results. Furthermore, it is confirmed to be more effective in running time.
%@language en
%3 WTD-SIBGRAPI2016-LilianBerton-2.pdf