Evolutionary Feature Programming: Cooperative learning for knowledge discovery and computer vision

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Book{Krawiec:book,
  author =       "Krzysztof Krawiec",
  title =        "Evolutionary Feature Programming: Cooperative learning
                 for knowledge discovery and computer vision",
  year =         "2004",
  publisher =    "Wydawnictwo Politechniki Poznanskiej",
  address =      "Poznan University of Technology, Poznan, Poland",
  number =       "385",
  series =       "{}",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://idss.cs.put.poznan.pl/~krawiec/pubs/hab/krawiec_hab.pdf",
  abstract =     "This book concerns the methodology of machine learning
                 algorithms that explicitly change the representation of
                 their training data while learning. This process, known
                 as feature construction or transformation of
                 representation, 'rewrites' learner's input data,
                 getting rid of useless data components, and combining
                 the useful ones in synergetic way with help of
                 background knowledge. The objective is to improve
                 learner's predictive performance and/or to enable
                 access to input data that is incompatible with learning
                 algorithm, and could not be used directly (e.g., raster
                 images).

                 The new methodology elaborated in this book, termed
                 evolutionary feature programming (EFP), puts the
                 feature construction task into optimisation perspective
                 and uses evolutionary computation to effectively search
                 the space of solutions. Each of the evolving
                 individuals represents a specific feature extraction
                 procedure. We design a novel variant of genetic
                 programming to encode the way the training data
                 undergoes transformation prior to being fed into
                 learning algorithm. The book provides extensive
                 rationale for this particular design and genetic
                 encoding of solutions.

                 Apart from this canonical approach, we propose a
                 methodology for tackling with complexity of EFP. In
                 coevolutionary feature programming (CFP), we decompose
                 the feature construction task using cooperative
                 coevolution, a variant of evolutionary computation that
                 allows for semi-independent elaboration of solution
                 components. We propose and discuss four different
                 decomposition strategies for breaking up the feature
                 construction process. The practical utility of EFP and
                 CFP is verified in two qualitatively different
                 application areas: machine learning from examples given
                 in attribute-value form, and visual learning from raw
                 raster images. Considered real-world case studies
                 concern glass type identification, diagnosing of
                 diabetes, sonar-based object identification, 3D object
                 recognition in visible spectrum, and vehicle
                 identification in radar modality. The experimental
                 results indicate that the proposed methodology is
                 general and proves effective in different environments,
                 and that it exhibits features that are appealing from
                 practical viewpoint (performance, scalability,
                 generalisation, explanatory character, to mention the
                 most important ones).",
  size =         "145 pages",
}

Genetic Programming entries for Krzysztof Krawiec

Citations