Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Iqbal:xd:ieeeTEC,
  author =       "Muhammad Iqbal and Bing Xue and Harith Al-Sahaf and 
                 Mengjie Zhang",
  title =        "Cross-Domain Reuse of Extracted Knowledge in Genetic
                 Programming for Image Classification",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2017",
  volume =       "21",
  number =       "4",
  pages =        "569--587",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Code
                 Fragments, Image Classification, Knowledge Extraction,
                 Building Blocks",
  ISSN =         "1089-778X",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7833127",
  DOI =          "doi:10.1109/TEVC.2017.2657556",
  size =         "19 pages",
  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.",
  notes =        "also known as \cite{7833127}",
}

Genetic Programming entries for Muhammad Iqbal Bing Xue Harith Al-Sahaf Mengjie Zhang

Citations