<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data

Department of Computer Science and Automation, Indian Institute of Science, Bangalore,

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The Modified Condensed Nearest Neighbour (MCNN) algorithm for prototype selection is order-independent, unlike the Condensed Nearest Neighbour (CNN) algorithm....

Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data

Publié en ligne: 20 mars 2017

Pages: 155 - 169

Reçu: 01 janv. 2016

Accepté: 04 juil. 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0011

Mots clés
prototype selection, one-pass algorithm, streaming data, distributed algorithm

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data

Publié en ligne: 20 mars 2017

Pages: 155 - 169

Reçu: 01 janv. 2016

Accepté: 04 juil. 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0011

Mots clésprototype selection, one-pass algorithm, streaming data, distributed algorithm

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
prototype selection, one-pass algorithm, streaming data, distributed algorithm