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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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