Effective Rule Induction from Molecular Structures Represented by Labeled Graphs

S Hoche, T Horvath, S Wrobel

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Acyclic conjunctive queries form a polynomially evaluable fragment of definite nonrecursive first-order Horn clauses. Labeled graphs, a special class of relational structures, provide a natural way for representing chemical compounds. We propose an algorithm specific to learning acyclic conjunctive queries predicting certain properties of molecules represented by labeled graphs. To compensate for the reduced expressive power of the hypothesis language and thus the potential decrease in classification accuracy, we combine acyclic conjunctive queries with confidence-rated boosting. This approach leads to excellent prediction accuracy on the domain of mutagenicity.
Translated title of the contributionEffective Rule Induction from Molecular Structures Represented by Labeled Graphs
Original languageEnglish
Title of host publicationUnknown
Publication statusPublished - Sep 2003

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 1st International Workshop on Mining Graphs, Trees and Sequences (MGTS-2003), Cavtat-Dubrovnik, Croatia

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