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2015 | OriginalPaper | Buchkapitel

Evolving Ensembles: What Can We Learn from Biological Mutualisms?

verfasst von : Michael A. Lones, Stuart E. Lacy, Stephen L. Smith

Erschienen in: Information Processing in Cells and Tissues

Verlag: Springer International Publishing

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Abstract

Ensembles are groups of classifiers which cooperate in order to reach a decision. Conventionally, the members of an ensemble are trained sequentially, and typically independently, and are not brought together until the final stages of ensemble generation. In this paper, we discuss the potential benefits of training classifiers together, so that they learn to interact at an early stage of their development. As a potential mechanism for achieving this, we consider the biological concept of mutualism, whereby cooperation emerges over the course of biological evolution. We also discuss potential mechanisms for implementing this approach within an evolutionary algorithm context.

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Metadaten
Titel
Evolving Ensembles: What Can We Learn from Biological Mutualisms?
verfasst von
Michael A. Lones
Stuart E. Lacy
Stephen L. Smith
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-23108-2_5