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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Online
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2083-2567
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Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data

V. Susheela Devi / Lakhpat Meena
Published Online: 2017-03-20 | DOI: https://doi.org/10.1515/jaiscr-2017-0011

Abstract

The Modified Condensed Nearest Neighbour (MCNN) algorithm for prototype selection is order-independent, unlike the Condensed Nearest Neighbour (CNN) algorithm. Though MCNN gives better performance, the time requirement is much higher than for CNN. To mitigate this, we propose a distributed approach called Parallel MCNN (pMCNN) which cuts down the time drastically while maintaining good performance. We have proposed two incremental algorithms using MCNN to carry out prototype selection on large and streaming data. The results of these algorithms using MCNN and pMCNN have been compared with an existing algorithm for streaming data.

Keywords: prototype selection; one-pass algorithm; streaming data; distributed algorithm

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About the article

Received: 2016-01-01

Accepted: 2016-07-04

Published Online: 2017-03-20

Published in Print: 2017-07-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2017-0011.

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© 2017 Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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