Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation

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Title: Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
Authors: Gallego, Antonio-Javier | 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: Efficient kNN classification | Clustering | Deep neural networks
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: Feb-2018
Publisher: Elsevier
Citation: Pattern Recognition. 2018, 74: 531-543. doi:10.1016/j.patcog.2017.09.038
Abstract: While standing as one of the most widely considered and successful supervised classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor efficiency due to being an instance-based method. In this sense, Approximated Similarity Search (ASS) stands as a possible alternative to improve those efficiency issues at the expense of typically lowering the performance of the classifier. In this paper we take as initial point an ASS strategy based on clustering. We then improve its performance by solving issues related to instances located close to the cluster boundaries by enlarging their size and considering the use of Deep Neural Networks for learning a suitable representation for the classification task at issue. Results using a collection of eight different datasets show that the combined use of these two strategies entails a significant improvement in the accuracy performance, with a considerable reduction in the number of distances needed to classify a sample in comparison to the basic kNN rule.
Sponsor: This work has been supported by the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds), the Spanish Ministerio de Educación, Cultura y Deporte through an FPU Fellowship (Ref. AP2012–0939), and by the Universidad de Alicante through the FPU program (UAFPU2014–5883 ) and the Instituto Universitario de Investigación Informática (IUII).
URI: http://hdl.handle.net/10045/70029
ISSN: 0031-3203 (Print) | 1873-5142 (Online)
DOI: 10.1016/j.patcog.2017.09.038
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 Elsevier Ltd.
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.patcog.2017.09.038
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