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.
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