Learning Classification Rules Using Lattices (Extended Abstract)

Sahami, Mehran

doi:10.1007/3-540-59286-5_83

Learning Classification Rules Using Lattices (Extended Abstract)

Mehran Sahami

ECML-PKDD 1995 pp. 343-346

doi:10.1007/3-540-59286-5_83 /ecmlpkdd/1995/sahami1995ecml-learning/

Abstract

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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Cite

Text

Sahami. "Learning Classification Rules Using Lattices (Extended Abstract)." European Conference on Machine Learning, 1995. doi:10.1007/3-540-59286-5_83

Markdown

[Sahami. "Learning Classification Rules Using Lattices (Extended Abstract)." European Conference on Machine Learning, 1995.](https://mlanthology.org/ecmlpkdd/1995/sahami1995ecml-learning/) doi:10.1007/3-540-59286-5_83

BibTeX

@inproceedings{sahami1995ecml-learning,
  title     = {{Learning Classification Rules Using Lattices (Extended Abstract)}},
  author    = {Sahami, Mehran},
  booktitle = {European Conference on Machine Learning},
  year      = {1995},
  pages     = {343-346},
  doi       = {10.1007/3-540-59286-5_83},
  url       = {https://mlanthology.org/ecmlpkdd/1995/sahami1995ecml-learning/}
}