Genetic programming for feature extraction and construction in image classification
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- @Article{FAN:2022:ASC,
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author = "Qinglan Fan and Ying Bi and Bing Xue and
Mengjie Zhang",
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title = "Genetic programming for feature extraction and
construction in image classification",
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journal = "Applied Soft Computing",
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year = "2022",
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volume = "118",
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pages = "108509",
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keywords = "genetic algorithms, genetic programming, Image
classification, Representation, Feature extraction,
Feature construction",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2022.108509",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494622000527",
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abstract = "Genetic Programming (GP) has been successfully applied
to image classification and achieved promising results.
However, most existing methods either address binary
image classification tasks only or need a predefined
classifier to perform multi-class image classification
while using GP for feature extraction. This limits
their flexibility since it is unknown which
combinations of classifiers and features are the most
effective for an image classification task.
Furthermore, high image variations increase the
difficulty of feature extraction and image
classification. This paper proposes a GP approach with
a new program representation, new functions, and new
terminals. The new approach can conduct feature
extraction, feature construction, and classification,
automatically and simultaneously. It can extract and
construct informative image features, select a suitable
classification algorithm instead of relying on a
predefined classifier, and perform classification for
binary and multi-class image classification tasks. In
addition, this paper develops a new mutation operator
based on fitness of population for dynamically
adjusting the size of the evolved GP programs. The
experimental results on eight datasets with different
variations and difficulties show that the proposed
approach achieves higher classification accuracy than
most of the benchmark methods. Further analysis shows
that the GP evolved programs have appropriate tree
sizes and potentially high interpretability",
- }
Genetic Programming entries for
Qinglan Fan
Ying Bi
Bing Xue
Mengjie Zhang
Citations