Learning Ensemble of Decision Trees through Multifactorial Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Wen:2016:CEC,
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author = "Yu-Wei Wen and Chuan-Kang Ting",
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title = "Learning Ensemble of Decision Trees through
Multifactorial Genetic Programming",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "5293--5300",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming,
Multifactorial evolution, ensemble learning, decision
tree",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7748363",
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size = "8 pages",
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abstract = "Genetic programming (GP) has received considerable
successes in machine learning tasks such as prediction
and classification. Ensemble learning enables the
collaboration of multiple classifiers and effectively
improves the classification accuracy. Learning an
ensemble of classifiers with GP can simply be achieved
by repeated runs of GP; however, the computational cost
will be multiplied as well. Recently, multifactorial
evolution was proposed to concurrently solve multiple
problems with a single population. This study uses the
multifactorial evolution and designs a multifactorial
genetic programming (MFGP) for efficiently learning an
ensemble of decision trees. In the MFGP, each task is
associated with one run of GP. The multifactorial
evolution enables MFGP to evolve multiple GP
classifiers for an ensemble in a single run, which
saves a substantial amount of computational cost at
repeated runs of GP. The experimental results show that
MFGP can learn an ensemble with comparable accuracy,
precision, and recall to conventional ensemble learning
methods, whereas MFGP requires much less computational
resource. The satisfactory outcomes validate the
advantages of MFGP in ensemble learning.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Yu-Wei Wen
Chuan-Kang Ting
Citations