A comparison of evolved linear and non-linear ensemble vote aggregators
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gp-bibliography.bib Revision:1.8098
- @InProceedings{lacy2015comparison,
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author = "Stuart E. Lacy and Michael A. Lones and
Stephen L. Smith",
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title = "A comparison of evolved linear and non-linear ensemble
vote aggregators",
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booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2015",
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pages = "758--763",
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ISSN = "1089-778X",
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month = may,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2015.7256967",
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abstract = "Ensemble classifiers have become a widely researched
area in machine learning because they are able to
generalise well to unseen data, making them suitable
for real world applications. Many approaches implement
simple voting techniques, such as majority voting or
averaging, to form the overall output. Alternatively,
Genetic Algorithms (GAs) can be used to train the
ensemble by optimising either binary or floating
weights in an average voting scheme to produce evolved
linear voting aggregators. Non-linear voting schemes
can also be formed by inputting the base classifiers'
outputs into a secondary classifier in the form of an
expression tree or an Artificial Neural Network; this
being subsequently evolved by an Evolutionary Algorithm
to optimise the ensemble prediction. This paper aims to
firstly, establish the impact of evolving linear
aggregators on traditional voting techniques, and
secondly whether these linear functions produce more
accurate predictions than more complex nonlinear
evolved aggregators. The results indicate that
optimising the ensemble combination method with GAs
offers significant advantages over standard approaches
and produces comparable ensembles to those with
nonlinear aggregators despite their additional
complexity.",
- }
Genetic Programming entries for
Stuart E Lacy
Michael A Lones
Stephen L Smith
Citations