Feature generation using genetic programming with application to fault classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/tsmc/GuoJN05,
  title =        "Feature generation using genetic programming with
                 application to fault classification",
  author =       "Hong Guo and Lindsay B. Jack and Asoke K. Nandi",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part B",
  year =         "2005",
  number =       "1",
  volume =       "35",
  pages =        "89--99",
  month =        feb,
  bibdate =      "2006-01-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/tsmc/tsmcb35.html#GuoJN05",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1083-4419",
  DOI =          "doi:10.1109/TSMCB.2004.841426",
  size =         "11 pages",
  abstract =     "One of the major challenges in pattern recognition
                 problems is the feature extraction process which
                 derives new features from existing features, or
                 directly from raw data in order to reduce the cost of
                 computation during the classification process, while
                 improving classifier efficiency. Most current feature
                 extraction techniques transform the original pattern
                 vector into a new vector with increased discrimination
                 capability but lower dimensionality. This is conducted
                 within a predefined feature space, and thus, has
                 limited searching power. Genetic programming (GP) can
                 generate new features from the original dataset without
                 prior knowledge of the probabilistic distribution. A
                 GP-based approach is developed for feature extraction
                 from raw vibration data recorded from a rotating
                 machine with six different conditions. The created
                 features are then used as the inputs to a neural
                 classifier for the identification of six bearing
                 conditions. Experimental results demonstrate the
                 ability of GP to discover automatically the different
                 bearing conditions using features expressed in the form
                 of nonlinear functions. Furthermore, four sets of
                 results-using GP extracted features with artificial
                 neural networks (ANN) and support vector machines
                 (SVM), as well as traditional features with ANN and
                 SVM-have been obtained. This GP-based approach is used
                 for bearing fault classification for the first time and
                 exhibits superior searching power over other
                 techniques. Additionally, it significantly reduces the
                 time for computation compared with genetic algorithm
                 (GA), therefore, makes a more practical realization of
                 the solution.",
}

Genetic Programming entries for Hong Guo Lindsay B Jack Asoke K Nandi

Citations