Genetic Programming for Image Classification: A New Program Representation with Flexible Feature Reuse
Created by W.Langdon from
gp-bibliography.bib Revision:1.6717
- @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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note = "Accepted for future publication",
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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’s
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