Abstract
AbstractInductive logic programming (ILP) induces concepts from a set of positive examples, a set of negative examples, and background knowledge. ILP has been applied on tasks such as natural language processing, finite element mesh design, network mining, robotics, and drug discovery. These data sets usually contain numerical and multivalued categorical attributes; however, only a few relational learning systems are capable of handling them in an efficient way. In this paper, we present an evolutionary approach, called Grouping and Discretization for Enriching the Background Knowledge (GDEBaK), to deal with numerical and multivalued categorical attributes in ILP. This method uses evolutionary operators to create and test numerical splits and subsets of categorical values in accordance with a fitness function. The best subintervals and subsets are added to the background knowledge before constructing candidate hypotheses. We implemented GDEBaK embedded in Aleph and compared it to lazy discretization in Aleph and discretization in Top‐down Induction of Logical Decision Trees (TILDE) systems. The results obtained showed that our method improves accuracy and reduces the number of rules in most cases. Finally, we discuss these results and possible lines for future work.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.