<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

Publicado en línea: 20 mar 2017

Páginas: 155 - 169

Recibido: 01 ene 2016

Aceptado: 04 jul 2016

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

Palabras clave
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

Publicado en línea: 20 mar 2017

Páginas: 155 - 169

Recibido: 01 ene 2016

Aceptado: 04 jul 2016

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

Palabras claveprototype 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.

Palabras clave
prototype selection, one-pass algorithm, streaming data, distributed algorithm