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Evolving ensembles of linear classifiers by means of clonal selection algorithm.
- Source :
- Control & Cybernetics; 2010, Vol. 39 Issue 2, p325-342, 18p, 5 Diagrams, 1 Chart
- Publication Year :
- 2010
-
Abstract
- Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers used to construct a set of classification rules. Aggregating strategies, such as bagging and boosting, are shown to work well with the proposed algorithm as the base classifier. [ABSTRACT FROM AUTHOR]
- Subjects :
- IMMUNE system
CLONAL selection theory
ALGORITHMS
LYMPHOCYTES
ANTIGENS
Subjects
Details
- Language :
- English
- ISSN :
- 03248569
- Volume :
- 39
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- Control & Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 52551239