Name
Title | ||
|---|---|---|
Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1515/jaiscr-2017-0011 | JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH |
Keywords | Field | DocType |
prototype selection,one-pass algorithm,streaming data,distributed algorithm | Computer science,Parallel computing,Distributed algorithm,Artificial intelligence,Streaming data,Computer engineering,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 3 | 2083-2567 |
Citations | PageRank | References |
2 | 0.38 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| V. Susheela Devi | 1 | 47 | 9.21 |
| Lakhpat Meena | 2 | 4 | 0.74 |