On the suitability of Prototype Selection methods for kNN classification with distributed data

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/55947
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Title: On the suitability of Prototype Selection methods for kNN classification with distributed data
Authors: Valero Mas, José Javier | Calvo-Zaragoza, Jorge | 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: Prototype Selection | Distributed data | k-Nearest Neighbour | Experimental study
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 26-Aug-2016
Publisher: Elsevier
Citation: Neurocomputing. 2016, 203: 150-160. doi:10.1016/j.neucom.2016.04.018
Abstract: In the current Information Age, data production and processing demands are ever increasing. This has motivated the appearance of large-scale distributed information. This phenomenon also applies to Pattern Recognition so that classic and common algorithms, such as the k-Nearest Neighbour, are unable to be used. To improve the efficiency of this classifier, Prototype Selection (PS) strategies can be used. Nevertheless, current PS algorithms were not designed to deal with distributed data, and their performance is therefore unknown under these conditions. This work is devoted to carrying out an experimental study on a simulated framework in which PS strategies can be compared under classical conditions as well as those expected in distributed scenarios. Our results report a general behaviour that is degraded as conditions approach to more realistic scenarios. However, our experiments also show that some methods are able to achieve a fairly similar performance to that of the non-distributed scenario. Thus, although there is a clear need for developing specific PS methodologies and algorithms for tackling these situations, those that reported a higher robustness against such conditions may be good candidates from which to start.
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 UE FEDER funds).
URI: http://hdl.handle.net/10045/55947
ISSN: 0925-2312 (Print) | 1872-8286 (Online)
DOI: 10.1016/j.neucom.2016.04.018
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 Elsevier B.V.
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.neucom.2016.04.018
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