A Divide-and-Conquer Genetic Programming Algorithm with Ensembles for Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Bi:TEVC2,
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author = "Ying Bi and Bing Xue and Mengjie Zhang",
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title = "A Divide-and-Conquer Genetic Programming Algorithm
with Ensembles for Image Classification",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2021",
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volume = "25",
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number = "6",
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pages = "1148--1162",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Feature
Learning, Knowledge Transfer, Ensemble Learning,
Divide-and-Conquer, Image Classification",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2021.3082112",
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size = "15 pages",
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abstract = "Genetic programming (GP) has been applied to feature
learning in image classification and achieved promising
results. However, one major limitation of existing
GP-based methods is the high computational cost, which
may limit their applications on large-scale image
classification tasks. To address this, this paper
develops a divide-and-conquer GP algorithm with
knowledge transfer and ensembles to achieve fast
feature learning in image classification. In the new
algorithm framework, a divideand-conquer strategy is
employed to split the training data and the population
into small subsets or groups to reduce computational
time. A new knowledge transfer method is proposed to
improve GP learning performance. A new fitness function
based on log-loss and a new ensemble formulation
strategy are developed to build an effective ensemble
for image classification. The performance of the
proposed approach has been examined on 12 image
classification datasets of varying difficulty. The
results show that the new approach achieves better
classification performance in significantly less
computation time than the baseline GP-based algorithm.
The comparisons with state-of-theart algorithms show
that the new approach achieves better or comparable
performance in almost all the comparisons. Further
analysis demonstrates the effectiveness of ensemble
formulation and knowledge transfer in the proposed
approach.",
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notes = "also known as \cite{9437306}",
- }
Genetic Programming entries for
Ying Bi
Bing Xue
Mengjie Zhang
Citations