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Exact fuzzy k-Nearest neighbor classification for big datasets

Authors

Jesus Maillo

Julian Luengo

Salvador

Francisco Herrera



Abstract

The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same importance to classify. There are several approaches to enhance its precision, with the Fuzzy k Nearest Neighbors (FuzzykNN) classifier being among the most successful ones. FuzzykNN computes a fuzzy degree of membership of each instance to the classes of the problem. As a result, it generates smoother borders between classes. Apart from the existing kNN approach to handle big datasets, there is not a fuzzy variant to manage that volume of data. Nevertheless, calculating this class membership adds an extra computational cost becoming even less scalable to tackle large datasets because of memory needs and high runtime. In this work, we present an exact and distributed approach to run the Fuzzy-kNN classifier on big datasets based on Spark, which provides the same precision than the original algorithm. It presents two separately stages. The first stage transforms the training set adding the class membership degrees. The second stage classifies with the kNN algorithm the test set using the class membership computed previously. In our experiments, we study the scaling-up capabilities of the proposed approach with datasets up to 11 million instances, showing promising results.

Citation

Maillo, J., Luengo, J., García, S., Herrera, F., & Triguero, I. (2017). Exact fuzzy k-Nearest neighbor classification for big datasets.

Conference Name IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)
End Date Jul 12, 2017
Acceptance Date Mar 14, 2017
Publication Date Jul 10, 2017
Deposit Date Aug 16, 2017
Publicly Available Date Aug 16, 2017
Peer Reviewed Peer Reviewed
Public URL https://nottingham-repository.worktribe.com/output/872258
Additional Information 1544-5615. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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