Genetic Programming for Image Classification: A New Program Representation with Flexible Feature Reuse
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gp-bibliography.bib Revision:1.7954
- @Article{QinglanFan:ieeeTEC,
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author = "Qinglan Fan and Ying Bi and Bing Xue and
Mengjie Zhang",
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title = "Genetic Programming for Image Classification: A New
Program Representation with Flexible Feature Reuse",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2023",
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volume = "27",
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number = "3",
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pages = "460--474",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Image
Classification,Feature Learning, Program Structure,
Feature Reuse",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2022.3169490",
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size = "15 pages",
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abstract = "Extracting effective features from images is crucial
for image classification, but it is challenging due to
high variations across images. Genetic programming (GP)
has become a promising machine learning approach to
feature learning in image classification. The
representation of existing GP-based image
classification methods is usually the tree-based
structure. These methods typically learn useful image
features according to the output of the GP program root
node. However, they are not flexible enough in feature
learning since the features produced by internal nodes
of the GP program have seldom been directly used. we
propose a new image classification approach using GP
with a new program structure, which can flexibly reuse
features generated from different nodes including
internal nodes of the GP program. The new method can
automatically learn various informative image features
based on the new function set and terminal set for
effective and efficient image classification.
Furthermore, instead of relying on a predefined
classification algorithm, the proposed approach can
automatically select a suitable classification
algorithm based on the learned features and conduct
classification simultaneously in a single evolved GP
program for an image classification task. The
experimental results on 12 benchmark datasets of
varying difficulty suggest that the new approach
achieves better performance than many state-of-the-art
methods. Further analysis demonstrates the
effectiveness and efficiency of the flexible feature
reuse in the proposed approach. The analysis of evolved
GP programs/solutions shows their potentially high
interpretability.",
-
notes = "also known as \cite{9761990}",
- }
Genetic Programming entries for
Qinglan Fan
Ying Bi
Bing Xue
Mengjie Zhang
Citations