Prototype generation on structural data using dissimilarity space representation

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Title: Prototype generation on structural data using dissimilarity space representation
Authors: Calvo-Zaragoza, Jorge | Valero Mas, José Javier | Rico Juan, Juan Ramón
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: kNN classification | Prototype generation | Structural pattern recognition | Dissimilarity space
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: Sep-2017
Publisher: Springer London
Citation: Neural Computing and Applications. 2017, 28(8): 2415-2424. doi:10.1007/s00521-016-2278-8
Abstract: Data reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, prototype selection (PS) and prototype generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: While the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees, or graphs. This work studies the possibility of using dissimilarity space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.
Sponsor: This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU fellowship (AP2012–0939), Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through FPU program (UAFPU2014–5883), and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds).
ISSN: 0941-0643 (Print) | 1433-3058 (Online)
DOI: 10.1007/s00521-016-2278-8
Language: eng
Type: info:eu-repo/semantics/article
Rights: © The Natural Computing Applications Forum 2016
Peer Review: si
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