REDA-ILP: Learning Theories using EDA and Reduced Bottom Clauses

نویسندگان

  • Cristiano Grijó Pitangui
  • Gerson Zaverucha
چکیده

In our previous work we have introduced EDA-ILP, an Inductive Logic Programming (ILP) system based on Estimation Distribution Algorithm (EDA). EDA-ILP showed to be superior when compared to GA-ILP, a variation of EDA-ILP created replacing the EDA by a “conventional” Genetic Algorithm. Additionally, EDA-ILP proved to be very competitive when compared to the state of the art ILP system Aleph. This work presents REDA-ILP, an extension of EDA-ILP that employs the well-known Reduce algorithm in order to considerably reduce the search space. Preliminary results show that REDA-ILP, when compared to EDA-ILP, achieves equivalent accuracies results while considerably reduces the search time in order to find simpler theories.

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تاریخ انتشار 2011