Susheela Devi, V and Meena, Lakhpat (2017) Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data. In: Journal of Artificial Intelligence and Soft Computing Research, 7 (3). pp. 155-169. ISSN 2083-2567
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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.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of Artificial Intelligence and Soft Computing Research |
| Publisher: | De Gruyter Open Ltd |
| Additional Information: | The Copyright of this article belongs to the Authors. |
| Keywords: | distributed algorithm; one-pass algorithm; prototype selection; streaming data |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 24 Jun 2022 10:10 |
| Last Modified: | 24 Jun 2022 10:10 |
| URI: | https://eprints.iisc.ac.in/id/eprint/73548 |
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