Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{conf/ausai/BhowanJZ11,
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author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
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title = "Ensemble Learning and Pruning in Multi-Objective
Genetic Programming for Classification with Unbalanced
Data",
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booktitle = "Proceedings of the 24th Australasian Joint Conference
Advances in Artificial Intelligence (AI 2011)",
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year = "2011",
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editor = "Dianhui Wang and Mark Reynolds",
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volume = "7106",
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series = "Lecture Notes in Computer Science",
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pages = "192--202",
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address = "Perth, Australia",
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month = dec # " 5-8",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1007/978-3-642-25832-9_20",
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size = "11 pages",
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abstract = "Machine learning algorithms can suffer a performance
bias when data sets are unbalanced. This paper develops
a multi-objective genetic programming approach to
evolving accurate and diverse ensembles of
non-dominated solutions where members vote on class
membership. We explore why the ensembles can also be
vulnerable to the learning bias using a range of
unbalanced data sets. Based on the notion that smaller
ensembles can be better than larger ensembles, we
develop a new evolutionary-based pruning method to find
groups of highly-cooperative individuals that can
improve accuracy on the important minority class.",
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affiliation = "Evolutionary Computation Research Group, Victoria
University of Wellington, New Zealand",
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bibdate = "2011-12-02",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#BhowanJZ11",
- }
Genetic Programming entries for
Urvesh Bhowan
Mark Johnston
Mengjie Zhang
Citations