An experimental study on rank methods for prototype selection

Please use this identifier to cite or link to this item:
Información del item - Informació de l'item - Item information
Title: An experimental study on rank methods for prototype selection
Authors: Valero Mas, José Javier | Calvo-Zaragoza, Jorge | Rico Juan, Juan Ramón | Iñesta, José M.
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: k-Nearest Neighbour | Data reduction | Prototype selection | Rank methods
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: Oct-2017
Publisher: Springer Berlin Heidelberg
Citation: Soft Computing. 2017, 21(19): 5703-5715. doi:10.1007/s00500-016-2148-4
Abstract: Prototype selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of maintaining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection according to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A representative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a number of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between prototype reduction and achieved accuracy.
Sponsor: This work has been supported by the Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through the FPU programme (UAFPU2014-5883), the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012-0939) and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds) and Consejería de Educación de la Comunidad Valenciana through project PROMETEO/2012/017.
ISSN: 1432-7643 (Print) | 1433-7479 (Online)
DOI: 10.1007/s00500-016-2148-4
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer-Verlag Berlin Heidelberg 2016
Peer Review: si
Publisher version:
Appears in Collections:INV - GRFIA - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2017_Valero-Mas_etal_SoftComput_final.pdfVersión final (acceso restringido)352,82 kBAdobe PDFOpen    Request a copy
Thumbnail2017_Valero-Mas_etal_SoftComput_preprint.pdfPreprint (acceso abierto)324,6 kBAdobe PDFOpen Preview

Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.