title: Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes.
abstract: The Growing Neural Gas model is used widely in artificial neural networks. However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs. Three parameters are added that allow the regulation of the restriction criteria. The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.
authors.affiliation: IBIS Research Group, University of Alicante, Alicante, Spain. Electronic address: yuriqp@ibisrg.com.
authors.forename: Yuri
authors.initials: Y
authors.lastname: Quintana-Pacheco
authors.affiliation: Department of Computing Technology, University of Alicante, Alicante, Spain. Electronic address: druiz@dtic.ua.es.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ruiz-Fernández
authors.affiliation: IBIS Research Group, University of Alicante, Alicante, Spain.
authors.forename: Agustín
authors.initials: A
authors.lastname: Magrans-Rico
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer
keywords: Growing Neural Gas; Prototype proliferation; Self-organizing model; Topology preservation