Rapid knowledge capture using subgroup discovery with incremental refinement

ABSTRACT

This paper presents an approach for rapid knowledge capture using subgroup-discovery techniques. The method enables the acquisition of scoring rules - a knowledge representation that is easy to understand and to maintain. Furthermore, the method features an incremental refinement step that can be applied for fine-tuning of the learned relations. We provide a case study demonstrating the applicability of the presented method using a knowledge base from the biological domain.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Design, Languages, Reliability, Theory

Keywords:
diagnostic scores, knowledge capture, subgroup discovery