Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Iqbal:xd:ieeeTEC,
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author = "Muhammad Iqbal and Bing Xue and Harith Al-Sahaf and
Mengjie Zhang",
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title = "Cross-Domain Reuse of Extracted Knowledge in Genetic
Programming for Image Classification",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2017",
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volume = "21",
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number = "4",
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pages = "569--587",
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month = aug,
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keywords = "genetic algorithms, genetic programming, Code
Fragments, Image Classification, Knowledge Extraction,
Building Blocks",
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ISSN = "1089-778X",
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URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7833127",
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DOI = "doi:10.1109/TEVC.2017.2657556",
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size = "19 pages",
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abstract = "Genetic programming (GP) is a well-known evolutionary
computation technique, which has been successfully used
to solve various problems, such as optimisation, image
analysis and classification. Transfer learning is a
type of machine learning approach that can be used to
solve complex tasks. Transfer learning has been
introduced to genetic programming to solve complex
Boolean and symbolic regression problems with some
promise. However, the use of transfer learning with
genetic programming has not been investigated to
address complex image classification tasks with noise
and rotations, where GP cannot achieve satisfactory
performance, but GP with transfer learning may improve
the performance. In this paper, we propose a novel
approach based on transfer learning and genetic
programming to solve complex image classification
problems by extracting and reusing blocks of
knowledge/information, which are automatically
discovered from similar as well as different image
classification tasks during the evolutionary process.
The proposed approach is evaluated on three texture
data sets and three office data sets of image
classification benchmarks, and achieves better
classification performance than the state-of-the-art
image classification algorithm. Further analysis on the
evolved solutions/trees shows that the proposed
approach with transfer learning can successfully
discover and reuse knowledge/information extracted from
similar or different problems to improve its
performance on complex image classification problems.",
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notes = "also known as \cite{7833127}",
- }
Genetic Programming entries for
Muhammad Iqbal
Bing Xue
Harith Al-Sahaf
Mengjie Zhang
Citations