Partial, Vague Knowledge for Similarity Measures

Steffens, Timo

Partial, Vague Knowledge for Similarity Measures

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Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 21-25.

Abstract

This paper proposes to enhance similarity-based classification by virtual attributes from imperfect domain theories. We analyze how properties of the domain theory, such as partialness and vagueness, influence classification accuracy. Experiments in a simple domain suggest that partial knowledge is more useful than vague knowledge. However, for data sets from the UCI Machine Learning Repository, we show that vague domain knowledge that in isolation performs at chance level can substantially increase classification accuracy when being incorporated into similarity-based classification.

Timo Steffens. 2005. Partial, Vague Knowledge for Similarity Measures. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 21–25. https://ijcai.org/proceedings/2005/39

@inproceedings{ijcai2005p39,
  title     = {Partial, Vague Knowledge for Similarity Measures},
  author    = {Steffens, Timo},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-05}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {21--25},
  year      = {2005},
  url       = {https://ijcai.org/proceedings/2005/39},
}

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TI  - Partial, Vague Knowledge for Similarity Measures
AU  - Steffens, Timo
T2  - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2005
SP  - 21
EP  - 25
UR  - https://ijcai.org/proceedings/2005/39
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