Active consensus-based semi-supervised growing neural gas

Active consensus-based semi-supervised growing neural gas

Author Maximo, Vinicius R. Autor UNIFESP Google Scholar
Nascimento, Mariá C. V. Autor UNIFESP Google Scholar
Breve, Fabricio A. Google Scholar
Quiles, Marcos G. Autor UNIFESP Google Scholar
Abstract In this paper, we propose a new active semi-supervised growing neural gas (GNG) model, named Active Consensus-Based Semi-Supervised GNG, or ACSSGNG. This model extends the former CSSGNG model by introducing an active mechanism for querying more representative samples in comparison to a random, or passive, selection. Moreover, as a semi-supervised model, the ACSSGNG takes both labelled and unlabelled samples in the training procedure. In comparison to other adaptations of the GNG to semi-supervised classification, the ACSSGNG does not assign a single scalar label value to each neuron. Instead, a vector containing the representativeness level of each class is associated with each neuron. Here, this information is used to select which sample the specialist might label instead of using a random selection of samples. Computer experiments show that our model can deliver, on average, better classification results than state-of-art semi-supervised algorithms, including the CSSGNG.
Language English
Sponsor Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Grant number FAPESP: 2011/18496-7
FAPESP: 2011/17396-9
FAPESP: 2015/21660-4
Date 2016
Published in Neural Information Processing, Iconip 2016, Pt Ii. Cham, v. 9948, p. 126-135, 2016.
ISSN 0302-9743 (Sherpa/Romeo, impact factor)
Publisher Karger
Extent 126-135
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000389805500015

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