Genetic programming with transfer learning for texture image classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{iqbal:SC,
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author = "Muhammad Iqbal and Harith Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "Genetic programming with transfer learning for texture
image classification",
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journal = "Soft Computing",
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year = "2019",
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volume = "23",
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number = "23",
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pages = "12859--12871",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Transfer
learning, Image classification, Code fragments,
Evolutionary computation",
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ISSN = "1432-7643",
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URL = "http://link.springer.com/article/10.1007/s00500-019-03843-5",
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DOI = "doi:10.1007/s00500-019-03843-5",
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size = "13 pages",
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abstract = "Genetic programming (GP) represents a well-known and
widely used evolutionary computation technique that has
shown promising results in optimisation,
classification, and symbolic regression problems.
However, similar to many other techniques, the
performance of GP deteriorates for solving highly
complex tasks. Transfer learning can improve the
learning ability of GP, which can be seen from previous
research on including, but not limited to, symbolic
regression and Boolean problems. However, using
transfer learning to tackle image-related,
specifically, image classification, problems in GP is
limited. This paper aims at proposing a new method for
employing transfer learning in GP to extract and
transfer knowledge in order to tackle complex texture
image classification problems. To assess the
improvement gained from using the extracted knowledge,
the proposed method is examined and compared against
the baseline GP method and a state-of-the-art method on
three publicly available and commonly used texture
image classification datasets. The obtained results
indicate that the reuse of the extracted knowledge from
an image dataset has significant impact on improving
the performance in learning different rotated versions
of the same dataset, as well as other related image
datasets. Further, it is found that the proposed
approach in the very first generation of the
evolutionary process produces better classification
accuracy than the final classification accuracy
obtained by the baseline method after 50 generations.",
- }
Genetic Programming entries for
Muhammad Iqbal
Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Citations