Effective Rule Induction from Molecular Structures Represented by Labeled Graphs.

Hoche, S., Horváth, T. and Wrobel, S. (2003) Effective Rule Induction from Molecular Structures Represented by Labeled Graphs. In: Proceedings of the 1st International Workshop on Mining Graphs, Trees and Sequences (MGTS-2003), 2003-09-22 - 2003-09-23.

Full text not available from this repository.

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 constrained confidence-rated boosting. Preliminary experimental results indicate the potential of the method for problems involving labeled graphs.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 23 Jul 2011 16:06
Last Modified: 18 May 2019 00:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/21939
DOI:

Actions (login required)

View Item