Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification
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- @Article{Bi:2020:CYB,
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author = "Ying Bi and Bing Xue and Mengjie Zhang",
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title = "Genetic Programming With a New Representation to
Automatically Learn Features and Evolve Ensembles for
Image Classification",
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journal = "IEEE Transactions on Cybernetics",
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year = "2021",
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volume = "51",
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number = "4",
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pages = "1769--1783",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Ensemble
learning, feature learning, image classification,
representation",
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ISSN = "2168-2275",
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DOI = "doi:10.1109/TCYB.2020.2964566",
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size = "15 pages",
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abstract = "Image classification is a popular task in machine
learning and computer vision, but it is very
challenging due to high variation crossing images.
Using ensemble methods for solving image classification
can achieve higher classification performance than
using a single classification algorithm. However, to
obtain a good ensemble, the component (base)
classifiers in an ensemble should be accurate and
diverse. To solve image classification effectively,
feature extraction is necessary to transform raw pixels
into high-level informative features. However, this
process often requires domain knowledge. This article
proposes an evolutionary approach based on genetic
programming to automatically and simultaneously learn
informative features and evolve effective ensembles for
image classification. The new approach takes raw images
as inputs and returns predictions of class labels based
on the evolved classifiers. To achieve this, a new
individual representation, a new function set, and a
new terminal set are developed to allow the new
approach to effectively find the best solution. More
important, the solutions of the new approach can
extract informative features from raw images and can
automatically address the diversity issue of the
ensembles. In addition, the new approach can
automatically select and optimize the parameters for
the classification algorithms in the ensemble. The
performance of the new approach is examined on 13
different image classification datasets of varying
difficulty and compared with a large number of
effective methods. The results show that the new
approach achieves better classification accuracy on
most datasets than the competitive methods. Further
analysis demonstrates that the new approach can evolve
solutions with high accuracy and diversity.",
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notes = "Also known as \cite{8976239}",
- }
Genetic Programming entries for
Ying Bi
Bing Xue
Mengjie Zhang
Citations