Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889811 | Neural Networks |
Keywords | DocType | ISSN |
pattern classification,self-organising feature maps,CSSGNG,OSSGNG models,consensus approach,consensus-based semisupervised GNG,consensus-based semisupervised growing neural gas,label propagation,self-organizing incremental network,semisupervised classification,semisupervised learning,unlabeled data | Conference | 2161-4393 |
ISBN | Citations | PageRank |
978-3-319-46671-2 | 2 | 0.38 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinicius R. Maximo | 1 | 2 | 0.38 |
| Marcos Quiles | 2 | 16 | 3.01 |
| Maria C. V. Nascimento | 3 | 2 | 0.38 |