RUSE-WARMR: Rule Selection for Classifier Induction in Multi-relational Data-Sets

Carlos Abreu Ferreira, João Gama and Vítor Santos Costa

November 2008

---

Abstract

One of the major challenges in knowledge discovery is how to extract meaningful and useful knowledge from the complex structured data that one finds in Scientific and Technological applications. One approach is to explore the logic relations in the database and using, say, an Inductive Logic Programming (ILP) algorithm find descriptive and expressive patterns. These patterns can then be used as features to characterize the target concept. The effectiveness of these algorithms depends both upon the algorithm we use to generate the patterns and upon the classifier. Rule mining provides an excellent framework for efficiently mining the interesting patterns that are relevant. We propose a novel method to select discriminative patterns and evaluate the effectiveness of this method on a complex discovery application of practical interest.

Bibtex

@InProceedings{ferreira-ictai08,
  author =    {C. A. Ferreira and J. Gama and V. Santos Costa},
  title =     {{RUSE-WARMR: Rule Selection for Classifier Induction in Multi-relational Data-Sets}},
  booktitle = {Proceedings of the 20th IEEE International Conference on Tools with Artificial
               Intelligence (ICTAI 2008)},
  pages     = {379--386},
  volume =    {1},
  publisher = {IEEE Computer Society},
  month =     {November},
  year =      {2008},
  address =   {Dayton, Ohio, USA},
}

Download Paper

PDF file
IEEE Computer Society