Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification

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Title: Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification
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: Nearest neighbor classification | Prototype Reduction | Promising classes
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: Oct-2017
Publisher: Springer Berlin Heidelberg
Citation: Soft Computing. 2017, 21(20): 6183-6189. doi:10.1007/s00500-016-2176-0
Abstract: The nearest neighbor rule is one of the most considered algorithms for supervised learning because of its simplicity and fair performance in most cases. However, this technique has a number of disadvantages, being the low computational efficiency the most prominent one. This paper presents a strategy to overcome this obstacle in multi-class classification tasks. This strategy proposes the use of Prototype Reduction algorithms that are capable of generating a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original training set using the nearest neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accuracy can be obtained compared to using the original training set, with a significantly higher efficiency.
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).
ISSN: 1432-7643 (Print) | 1433-7479 (Online)
DOI: 10.1007/s00500-016-2176-0
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer-Verlag Berlin Heidelberg 2016
Peer Review: si
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