Abstract
In this work, we focus on the Dendritic Cell Algorithm (DCA), a bio-inspired classifier, and its limitation when coping with very large datasets. To overcome this limitation, we propose a novel distributed DCA version for data classification based on the MapReduce framework to distribute the functioning of this algorithm
through a cluster of computing elements. Our experimental results show that our proposed distributed solution is suitable to enhance the performance of the DCA enabling the algorithm to be applied over big data classification problems.
through a cluster of computing elements. Our experimental results show that our proposed distributed solution is suitable to enhance the performance of the DCA enabling the algorithm to be applied over big data classification problems.
| Original language | English |
|---|---|
| Title of host publication | GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion |
| Subtitle of host publication | GECCO |
| Publisher | Association for Computing Machinery |
| Pages | 103-104 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450357647 |
| DOIs | |
| Publication status | Published - 06 Jul 2018 |
| Event | GECCO 2018: The Genetic and Evolutionary Computation Conference - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018 http://gecco-2018.sigevo.org |
Conference
| Conference | GECCO 2018: The Genetic and Evolutionary Computation Conference |
|---|---|
| Country/Territory | Japan |
| City | Kyoto |
| Period | 15 Jul 2018 → 19 Jul 2018 |
| Internet address |
Keywords
- Dendritic Cell Algorithm
- Classification
- Big Data
- Distributed Processing
- Scalability