Context-sensitive refinements for stochasticoptimization algorithms in inductive logic programming

ABSTRACT

In this paper we describe a new approach to the application of evolutionary stochastic search in Inductive Logic Programming (ILP). Unlike traditional approaches that focus on evolving populations of logical clauses, our refinement-based approach uses the stochastic optimization process to iteratively adapt initial working clause. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts and enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach.

References

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3. Žáková, M., Železný, F., Garcia-Sedano, J., Tissot, C. M., Lavrač, N., KYemen, P. and Molina, J. Relational data mining applied to virtual engineering of product designs. International Conference on ILP (ILP '07). Springer, 2007.

Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Logical and relational learning

        • Inductive logic learning