Adapting to Concept Drift with Genetic Programming for Classifying Streaming Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Smith:2016:CEC,
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author = "Murray Smith and Vic Ciesielski",
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title = "Adapting to Concept Drift with Genetic Programming for
Classifying Streaming Data",
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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 = "5026--5033",
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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",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7748327",
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abstract = "Concept drift in data streams is a change in the
underlying distribution that can cause algorithms that
are classifying them to have increased error. There is
a need for algorithms that can adapt to these changes.
Genetic programming is one such algorithm that can
adapt to streaming data however its use in this area is
somewhat unexplored. It is hoped that because genetic
programming is a population based method the variety of
solutions tested every generation will enable it to
adapt quickly. The adaptation speed can be increased by
determining suitable parameters and settings. In order
to test these ideas, experiments were run on several
synthetic and one world streaming data set. The results
found that genetic programming was capable of adapting
quickly to concept drift and that increased",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Murray Smith
Victor Ciesielski
Citations