Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Maximum Common Subgraph based locally weighted regression

      Seeland, Madeleine; Buchwald, Fabian; Kramer, Stefan; Pfahringer, Bernhard
      DOI
       10.1145/2245276.2245309
      Find in your library  
      Citation
      Export citation
      Seeland, M., Buchwald, F., Kramer, S. & Pfahringer, B. (2012). Maximum Common Subgraph based locally weighted regression. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC '12). ACM, New York, 165-172.
      Permanent Research Commons link: https://hdl.handle.net/10289/6631
      Abstract
      This paper investigates a simple, yet effective method for regression on graphs, in particular for applications in chem-informatics and for quantitative structure-activity relationships (QSARs). The method combines Locally Weighted Learning (LWL) with Maximum Common Subgraph (MCS) based graph distances. More specifically, we investigate a variant of locally weighted regression on graphs (structures) that uses the maximum common subgraph for determining and weighting the neighborhood of a graph and feature vectors for the actual regression model. We show that this combination, LWL-MCS, outperforms other methods that use the local neighborhood of graphs for regression. The performance of this method on graphs suggests it might be useful for other types of structured data as well.
      Date
      2012
      Type
      Conference Contribution
      Publisher
      ACM
      Collections
      • Computing and Mathematical Sciences Papers [1454]
      Show full item record  

      Usage

       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement