|Título||Relational Case-based Reasoning for Cancinogenic Activity Prediction|
|Publication Type||Journal Article|
|Year of Publication||2003|
|Authors||Armengol E, Plaza E|
|Journal||Artificial Intelligence Review|
Lazy learning methods are based on retrieving a set of cases similar to a new case. An important issue of these methods is how to estimate the similarity among a new case and the precedents. Most of work on similarities considers that the cases have a propositional representation. In this paper we present Shaud, a similarity measure useful to estimate the similarity among relational cases represented using feature terms. Also we present some preliminary results of the application of Shaud for solving classification tasks. In particular we used Shaud for assessing the carcinogenic activity of chemical compounds in the Toxicology dataset.
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