Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach

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  author =       "Philip G. K. Reiser and Patricia J. Riddle",
  title =        "Scaling Up Inductive Logic Programming: An
                 Evolutionary Wrapper Approach",
  journal =      "Applied Intelligence",
  year =         "2001",
  volume =       "15",
  number =       "3",
  pages =        "181--197",
  month =        nov # "-" # dec,
  note =         "Special Issue: Simulated Evolution and Learning",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, inductive logic programming, sampling,
                 machine learning, ILP",
  ISSN =         "0924-669X",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1023/A:1011239013893",
  size =         "17 pages",
  abstract =     "Inductive logic programming (ILP) algorithms are
                 classification algorithms that construct classifiers
                 represented as logic programs. ILP algorithms have a
                 number of attractive features, notably the ability to
                 make use of declarative background (user-supplied)
                 knowledge. However, ILP algorithms deal poorly with
                 large data sets (>10000 examples) and their widespread
                 use of the greedy set-covering algorithm renders them
                 susceptible to local maxima in the space of logic

                 This paper presents a novel approach to address these
                 problems based on combining the local search properties
                 of an inductive logic programming algorithm with the
                 global search properties of an evolutionary algorithm.
                 The proposed algorithm may be viewed as an evolutionary
                 wrapper around a population of ILP algorithms.

                 The evolutionary wrapper approach is evaluated on two
                 domains. The chess-endgame (KRK) problem is an
                 artificial domain that is a widely used benchmark in
                 inductive logic programming, and Part-of-Speech Tagging
                 is a real-world problem from the field of Natural
                 Language Processing. In the latter domain, data
                 originates from excerpts of the Wall Street Journal.
                 Results indicate that significant improvements in
                 predictive accuracy can be achieved over a conventional
                 ILP approach when data is plentiful and noisy.",
  notes =        "Article ID: 354285


Genetic Programming entries for Philip G K Reiser Patricia Jean Riddle