Particle swarm optimisation for object classification
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- @InProceedings{Evans:2008:IVCNZ,
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author = "H. Evans and Mengjie Zhang",
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title = "Particle swarm optimisation for object
classification",
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booktitle = "23rd International Conference Image and Vision
Computing New Zealand, IVCNZ 2008",
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year = "2008",
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month = nov,
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pages = "1--6",
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keywords = "genetic algorithms, genetic programming, PSO, feature
partitioning, noise factor, object classification,
optimal partition matrix, particle swarm optimisation,
weight matrix, feature extraction, image
classification, object detection, particle swarm
optimisation",
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DOI = "doi:10.1109/IVCNZ.2008.4762143",
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abstract = "This paper describes a new approach to the use of
particle swarm optimisation (PSO) for object
classification problems. Instead of using PSO to evolve
only a set of good parameter values for another machine
learning method for object classification, the new
approach developed in this paper can be used as a stand
alone method for classification. Two new methods are
developed in the new approach. The first new PSO method
treats all different features equally important and
finds an optimal partition matrix to separate a data
set into distinct class groups. The second new PSO
method considers the relative importance of each
feature with the noise factor, and evolves a weight
matrix to mitigate the effects of noisy partitions and
feature dimensions. The two methods are examined and
compared with a popular method using PSO combined with
the nearest centroid and another evolutionary computing
method, genetic programming, on three image data sets
of increasing difficulty. The results suggest that the
new weighted PSO method outperforms these existing
methods on these object classification problems.",
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notes = "Refers to \cite{zhang:2004:eurogp} Also known as
\cite{4762143}",
- }
Genetic Programming entries for
Hamish Evans
Mengjie Zhang
Citations