Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
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- @Article{sun:2024:AS,
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author = "Yu Sun and Zhiqiang Zhang",
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title = "Automatic Feature {Construction-Based} Genetic
Programming for Degraded Image Classification",
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journal = "Applied Sciences",
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year = "2024",
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volume = "14",
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number = "4",
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pages = "Article No. 1613",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/14/4/1613",
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DOI = "doi:10.3390/app14041613",
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abstract = "Accurately classifying degraded images is a
challenging task that relies on domain expertise to
devise effective image processing techniques for
various levels of degradation. Genetic Programming (GP)
has been proven to be an excellent approach for solving
image classification tasks. However, the program
structures designed in current GP-based methods are not
effective in classifying images with quality
degradation. During the iterative process of GP
algorithms, the high similarity between individuals
often results in convergence to local optima, hindering
the discovery of the best solutions. Moreover, the
varied degrees of image quality degradation often lead
to overfitting in the solutions derived by GP.
Therefore, this research introduces an innovative
program structure, distinct from the traditional
program structure, which automates the creation of new
features by transmitting information learnt across
multiple nodes, thus improving GP individual ability in
constructing discriminative features. An accompanying
evolution strategy addresses high similarity among GP
individuals by retaining promising ones, thereby
refining the algorithm's development of more effective
GP solutions. To counter the potential overfitting
issue of the best GP individual, a multi-generational
individual ensemble strategy is proposed, focusing on
constructing an ensemble GP individual with an enhanced
generalisation capability. The new method evaluates
performance in original, blurry, low contrast, noisy,
and occlusion scenarios for six different types of
datasets. It compares with a multitude of effective
methods. The results show that the new method achieves
better classification performance on degraded images
compared with the comparative methods.",
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notes = "also known as \cite{app14041613}",
- }
Genetic Programming entries for
Yu Sun
Zhiqiang Zhang
Citations