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Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Active Consensus-Based Semi-supervised Growing Neural Gas

Authors : Vinícius R. Máximo, Mariá C. V. Nascimento, Fabricio A. Breve, Marcos G. Quiles

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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.

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Metadata
Title
Active Consensus-Based Semi-supervised Growing Neural Gas
Authors
Vinícius R. Máximo
Mariá C. V. Nascimento
Fabricio A. Breve
Marcos G. Quiles
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_15

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