Learning classification rules using lattices (Extended abstract)

  • Mehran Sahami
Extended Abstracts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 912)


This paper presents a novel induction algorithm, Rulearner, which induces classification rules using a Galois lattice as an explicit map through the search space of rules. The Rulearner system is shown to compare favorably with commonly used symbolic learning methods which use heuristics rather than an explicit map to guide their search through the rule space. Furthermore, our learning system is shown to be robust in the presence of noisy data. The Rulearner system is also capable of learning both decision lists and unordered rule sets allowing for comparisons of these different learning paradigms within the same algorithmic framework.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Mehran Sahami
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanfordUSA

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