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Evolving ensembles of linear classifiers by means of clonal selection algorithm

Bereta M., Burczyński T.

Control and Cybernetics

2010

Vol. 39, no 2

325-342

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.