Lazy Induction of Descriptions for Relational Case-Based Learning
Abstract
Reasoning and learning from cases are based on the concept of similarity often estimated by a distance. This paper presents LID, a learning technique adequate for domains where cases are best represented by relations among entities. LID is able to 1) define a similitude term , a symbolic description of what is shared between a problem and precedent cases; and 2) assess the importance of the relations involved in a similitude term with respect to the purpose of correctly classifying the problem. The paper describes two application domains of relational case-based learning with LID: marine sponges identification and diabetes risk assessment.
Cite
Text
Armengol and Plaza. "Lazy Induction of Descriptions for Relational Case-Based Learning." European Conference on Machine Learning, 2001. doi:10.1007/3-540-44795-4_2Markdown
[Armengol and Plaza. "Lazy Induction of Descriptions for Relational Case-Based Learning." European Conference on Machine Learning, 2001.](https://mlanthology.org/ecmlpkdd/2001/armengol2001ecml-lazy/) doi:10.1007/3-540-44795-4_2BibTeX
@inproceedings{armengol2001ecml-lazy,
title = {{Lazy Induction of Descriptions for Relational Case-Based Learning}},
author = {Armengol, Eva and Plaza, Enric},
booktitle = {European Conference on Machine Learning},
year = {2001},
pages = {13-24},
doi = {10.1007/3-540-44795-4_2},
url = {https://mlanthology.org/ecmlpkdd/2001/armengol2001ecml-lazy/}
}