Forming classifier ensembles with multimodal evolutionary algorithms
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- @InProceedings{lacy2015forming,
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title = "Forming classifier ensembles with multimodal
evolutionary algorithms",
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author = "Stuart E. Lacy and Michael A. Lones and
Stephen L. Smith",
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booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2015",
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pages = "723--729",
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month = "25-28 " # may,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/CEC.2015.7256962",
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size = "7 pages",
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abstract = "Ensemble classifiers have become popular in recent
years owing to their ability to produce robust
predictive models that generalise well to previously
unseen data. In principle, Evolutionary Algorithms
(EAs) are well suited to ensemble generation since they
result in a pool of trained classifiers. However, in
practice they are infrequently used for this purpose.
Current research trends in the EA community focus on
relatively complex mechanisms for building ensembles,
such as co-evolution and multi-objective optimisation.
In this paper, we take a back-to-basics approach,
studying whether conventional EAs, augmented with
simple niching strategies, can be used to form accurate
ensembles. We focus on crowding for this, considering
both deterministic and probabilistic variants. We also
consider the effect of different similarity measures.
Our results suggest that simple niching methods can
lead to accurate ensemble classifiers and that the
choice of similarity measure is not a significant
factor. A further study using heterogeneous classifier
models within the population showed no added benefit.",
- }
Genetic Programming entries for
Stuart E Lacy
Michael A Lones
Stephen L Smith
Citations