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Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data


Jesus Maillo



Francisco Herrera


One of the best-known and most effective methods in supervised classification is the k nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, highlighting the classical Fuzzy k nearest neighbors (FkNN). However, these traditional algorithms fail to tackle the large amounts of data that are available today. There are multiple alternatives to enable kNN classification in big datasets, spotlighting the approximate version of kNN called Hybrid Spill Tree. Nevertheless, the existing proposals of FkNN for big data problems are not fully scalable, because a high computational load is required to obtain the same behavior as the original FkNN algorithm. This work proposes Global Approximate Hybrid Spill Tree FkNN and Local Hybrid Spill Tree FkNN, two approximate approaches that speed up runtime without losing quality in the classification process. The experimentation compares various FkNN approaches for big data with datasets of up to 11 million instances. The results show an improvement in runtime and accuracy over literature algorithms.


Maillo, J., García, S., Luengo, J., Herrera, F., & Triguero, I. (2020). Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data. IEEE Transactions on Fuzzy Systems, 28(5), 874-886.

Journal Article Type Article
Acceptance Date Aug 9, 2019
Online Publication Date Aug 20, 2019
Publication Date 2020-05
Deposit Date Aug 23, 2019
Publicly Available Date Aug 23, 2019
Journal IEEE Transactions on Fuzzy Systems
Print ISSN 1063-6706
Electronic ISSN 1941-0034
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 28
Issue 5
Pages 874-886
Keywords Index Terms-Fuzzy sets; k nearest neighbors; Classification; MapReduce; Apache Spark; Big Data
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