Relational Case-based Reasoning for Carcinogenic Activity Prediction
Copyright policy )Relational Case-based Reasoning for Carcinogenic Activity Prediction
Lazy learning methods are based on retrieving a set of precedent 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. Usually, similarity measures require that cases have a prepositional representation. In this paper we present Shaud, a similarity measure useful to estimate the similarity among relational cases represented using feature terms. We also present results of the application of Shaud for solving classification tasks. Specifically we used Shaud for assessing the carcinogenic activity of chemical compounds in the Toxicology dataset.
This work has been supported by the projects IBROW (IST-1999-19005) and SAMAP (TIC2002-04146-C05-01).
Peer Reviewed
Lazy learning methods, Toxicology dataset, Feature terms, Machine learning, Similarity assessment
Lazy learning methods, Toxicology dataset, Feature terms, Machine learning, Similarity assessment
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