Genetic Programming for Image Classification with Unbalanced Data
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Bhowan:2009:IVCNZ,
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title = "Genetic Programming for Image Classification with
Unbalanced Data",
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author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
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booktitle = "Proceeding of the 24th International Conference Image
and Vision Computing New Zealand, IVCNZ '09",
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year = "2009",
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month = "23-25 " # nov,
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pages = "316--321",
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ISSN = "2151-2205",
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address = "Wellington",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-4697-1",
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DOI = "doi:10.1109/IVCNZ.2009.5378388",
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abstract = "Image classification methods using unbalanced data can
produce results with a performance bias. If the class
representing important objects-of-interest is in the
minority class, learning methods can produce the
deceptive appearance of good looking results while
recognition ability on the important minority class can
be poor. This paper develops and compares two Genetic
Programming (GP) methods for image classification
problems with class imbalance. The first focuses on
adapting the fitness function in GP to evolve
classifiers with good individual class accuracy. The
second uses a multi-objective approach to
simultaneously evolve a set of classifiers along the
trade-off surface representing minority and majority
class accuracies. Evaluating our GP methods on two
benchmark binary image classification problems with
class imbalance, our results show that good solutions
were evolved using both GP methods.",
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notes = "Also known as \cite{5378388}",
- }
Genetic Programming entries for
Urvesh Bhowan
Mengjie Zhang
Mark Johnston
Citations