Multiclass Object Classification Using Genetic Programming
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gp-bibliography.bib Revision:1.8051
- @InProceedings{zhang:evows04,
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author = "Mengjie Zhang and Will Smart",
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title = "Multiclass Object Classification Using Genetic
Programming",
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booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoMUSART}, {EvoSTOC}",
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year = "2004",
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month = "5-7 " # apr,
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editor = "Guenther R. Raidl and Stefano Cagnoni and
Jurgen Branke and David W. Corne and Rolf Drechsler and
Yaochu Jin and Colin R. Johnson and Penousal Machado and
Elena Marchiori and Franz Rothlauf and George D. Smith and
Giovanni Squillero",
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series = "LNCS",
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volume = "3005",
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address = "Coimbra, Portugal",
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publisher = "Springer Verlag",
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publisher_address = "Berlin",
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pages = "369--378",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation",
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ISBN = "3-540-21378-3",
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DOI = "doi:10.1007/978-3-540-24653-4_38",
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abstract = "We describe an approach to the use of genetic
programming for multiclass object classification
problems. Rather than using fixed static thresholds as
boundaries to distinguish between different classes,
this approach introduces two methods of classification
where the boundaries between different classes can be
dynamically determined during the evolutionary process.
The two methods are centred dynamic class boundary
determination and slotted dynamic class boundary
determination. The two methods are tested on four
object classification problems of increasing difficulty
and are compared with the commonly used static class
boundary determination method. The results suggest
that, while the static class boundary determination
method works well on relatively easy object
classification problems, the two dynamic class boundary
determination methods outperform the static method for
more difficult multiple class object classification
problems.",
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notes = "EvoWorkshops2004",
- }
Genetic Programming entries for
Mengjie Zhang
Will Smart
Citations