A fast k-nearest neighbor classifier using unsupervised clustering
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
12-16-2016
Abstract
In this paper we propose a fast method to classify patterns when using a k-nearest neighbor (kNN) classifier. The kNN classifier is one of the most popular supervised classification strategies. It is easy to implement, and easy to use. However, for large training data sets, the process can be time consuming due to the distance calculation of each test sample to the training samples. Our goal is to provide a generic method to use the same classification strategy, but considerably speed up the distance calculation process. First, the training data is clustered in an unsupervised manner to find the ideal cluster setup to minimize the intra-class dispersion, using the so-called “jump” method. Once the clusters are defined, an iterative method is applied to select some percentage of the data closest to the cluster centers and furthest from the cluster centers, respectively. Beside some interesting property discovered by altering the different selection criteria, we proved the efficiency of the method by reducing by up to 71% the classification speed, while keeping the classification performance in the same range.
Recommended Citation
Vajda S., Santosh K.C. (2017) A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering. In: Santosh K., Hangarge M., Bevilacqua V., Negi A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_17
Journal
RTIP2R 2016
Rights
© Springer Nature Singapore Pte Ltd. 2017
Comments
This article was originally published in 2016 International Conference on Recent Trends in Image Processing and Pattern Recognition. The full-text article from the publisher can be found here.
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