Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation. (February 2018)

• Record Type: Journal Article
• Authors: Gallego, Antonio-Javier; Calvo-Zaragoza, Jorge; Valero-Mas, Jose J.; Rico-Juan, Juan R.
• Is Part Of: Pattern recognition. Volume 74(2018:Feb.)
• Page Start: 531
• Page End: 543
• Publication Date: 2018-02
• Subjects: Efficient kNN classification -- Clustering -- Deep neural networks; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.09.038
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new scheme to conduct cluster-based kNN search is proposed. It includes the use of Deep Neural Networks for feature extraction. Comprehensive experimentation and analysis is presented. We demonstrate that the strategy is able to achieve better classification rates. Abstract: While standing as one of the most widely considered and successful supervised classification algorithms, the k -nearest Neighbor ( k NN) 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 k NN rule.