A genetic programming-based method for image classification with small training data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{FAN:2024:knosys,
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author = "Qinglan Fan and Ying Bi and Bing Xue and
Mengjie Zhang",
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title = "A genetic programming-based method for image
classification with small training data",
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year = "2024",
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journal = "Knowledge-Based Systems",
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volume = "283",
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pages = "111188",
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keywords = "genetic algorithms, genetic programming, Image
classification, Fitness function, Crossover",
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ISSN = "0950-7051",
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URL = "https://www.sciencedirect.com/science/article/pii/S0950705123009383",
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DOI = "doi:10.1016/j.knosys.2023.111188",
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abstract = "Genetic programming (GP) has been considerably used
for image classification because of its ability to
learn simple and effective models. However, most GP
methods require a large amount of training data to
learn informative features for classification, where
the generalization performance might be poor when only
a few training instances are available. In addition to
using classification accuracy to assess the goodness of
GP individuals/solutions like in most GP methods, this
paper proposes a new fitness function containing
distance measures. The proposed method uses different
distance measures to deal with binary and multi-class
classification automatically. By simultaneously
minimising the within-class distance and maximising the
between-class distance, the generalisation performance
can be improved. Furthermore, existing GP methods
typically employ standard crossover to search for the
best individuals from the whole search space. However,
these methods might not completely exploit the
potential local search space. Based on the niching
technique, this paper develops a new crossover
operator, which enables better exploitation of the
global and local search space, improving learning
effectiveness and classification accuracy. The new
approach achieves significantly better generalization
performance than almost all benchmark methods on eight
datasets and is also computationally efficient. Further
analysis demonstrates the significance of the new
fitness function and crossover operator and shows the
potentially good interpretability of the learned
models",
- }
Genetic Programming entries for
Qinglan Fan
Ying Bi
Bing Xue
Mengjie Zhang
Citations