Adapting Bagging and Boosting to Learning Classifier Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Liu:2018:evoApplications,
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author = "Yi Liu2 and Will N. Browne and Bing Xue",
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title = "Adapting Bagging and Boosting to Learning Classifier
Systems",
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booktitle = "21st International Conference on the Applications of
Evolutionary Computation, EvoIASP 2018",
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year = "2018",
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editor = "Stefano Cagnoni and Mengjie Zhang",
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series = "LNCS",
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volume = "10784",
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publisher = "Springer",
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pages = "405--420",
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address = "Parma, Italy",
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month = "4-6 " # apr,
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organisation = "Species",
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keywords = "genetic algorithms, genetic programming, Learning
classifier systems, Multiple domain learning, Ensemble
learning",
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isbn13 = "978-3-319-77537-1",
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DOI = "doi:10.1007/978-3-319-77538-8_28",
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abstract = "Learning Classifier Systems (LCSs) have demonstrated
their classification capability by employing a
population of polymorphic rules in addressing numerous
benchmark problems. However, although the produced
solution is often accurate, the alternative ways to
represent the data in a single population obscure the
underlying patterns of a problem. Moreover, once a
population is dominated by over-general rules, the
system will sink into the local optimal trap. To grant
a problem's patterns more transparency, the redundant
rules and optimal rules need to be distinguished.
Therefore, the bagging method is introduced to LCSs
with the aim to reduce the variance associated with
redundant rules. A novel rule reduction method is
proposed to reduce the rules' polymorphism in a
problem. This is tested with complex binary problems
with typical epistatic, over-lapping niches,
niche-imbalance, and specific-addiction properties at
various scales. The results show the successful
highlighting of the patterns for all the tested
problems, which have been addressed successfully.
Moreover, by combining the boosting method with LCSs,
the hybrid system could adjust previously defective
solutions such that they now represent the correct
classification of data.",
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notes = "EvoApplications2018 held in conjunction with
EuroGP'2018 EvoCOP2018 and EvoMusArt2018
http://www.evostar.org/2018/cfp_evoapps.php",
- }
Genetic Programming entries for
Yi Liu2
Will N Browne
Bing Xue
Citations