Fitness Functions in Genetic Programming for Classification with Unbalanced Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{DBLP:conf/ausai/PattersonZ07,
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author = "Grant Patterson and Mengjie Zhang",
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title = "Fitness Functions in Genetic Programming for
Classification with Unbalanced Data",
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booktitle = "Proceedings of the 20th Australian Joint Conference on
Artificial Intelligence",
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year = "2007",
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editor = "Mehmet A. Orgun and John Thornton",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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pages = "769--775",
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address = "Gold Coast, Australia",
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month = dec # " 2-6",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-540-76926-2",
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DOI = "doi:10.1007/978-3-540-76928-6_90",
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size = "11 pages",
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abstract = "This paper describes a genetic programming (GP)
approach to binary classification with class imbalance
problems. This approach is examined on two benchmark
and two synthetic data sets. The results show that when
using the overall classification accuracy as the
fitness function, the GP system is strongly biased
toward the majority class. Two new fitness functions
are developed to deal with the class imbalance problem.
The experimental results show that both of them
substantially improve the performance for the minority
class, and the performance for the majority and
minority classes is much more balanced.",
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notes = "Pima",
- }
Genetic Programming entries for
Grant Patterson
Mengjie Zhang
Citations