Kernels on Lists and Sets over Relational Algebra: An Application to Classification of Protein Fingerprints

  • Adam Woźnica
  • Alexandros Kalousis
  • Melanie Hilario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


In this paper we propose a new class of kernels defined over extended relational algebra structures. The “extension” was recently proposed in [1] and it overcomes one of the main limitation of the standard relational algebra, i.e. difficulties in modeling lists. These new kernels belong to the class of \(\mathcal{R}\)-Convolution kernels in the sense that the computation of the similarity between two complex objects is based on the similarities of objects’ parts computed by means of subkernels. The complex objects (relational instances in our case) are tuples and sets and/or lists of relational instances for which elementary kernels and kernels on sets and lists are applied. The performance of this class of kernels together with the Support Vector Machines (SVM) algorithm is evaluated on the problem of classification of protein fingerprints and by combining different data representations we were able to improve the best accuracy reported so far in the literature.


Support Vector Machine Complex Object Relational Algebra Convolution Kernel Type List 


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  1. 1.
    Woźnica, A., Kalousis, A., Hilario, M.: Distance-based learning over extended relational algebra structures. In: Proceedings of the 15th International Conference on Inductive Logic Programming (late breaking papers), Bonn, Germany (2005)Google Scholar
  2. 2.
    Haussler, D.: Convolution kernels on discrete structures. Technical report, UC Santa Cruz (1999)Google Scholar
  3. 3.
    Gaertner, T., Lloyd, J., Flach, P.: Kernels and distances for structured data. Machine Learning (2004)Google Scholar
  4. 4.
    Schölkopf, B., Tsuda, K., Vert, J.: Kernel Methods in Computational Biology. In: MIT Press series on Computational Molecular Biology. MIT Press, Cambridge (2003)Google Scholar
  5. 5.
    Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. Journal of Machine Learning Research 3, 1083–1106 (2003)MathSciNetMATHGoogle Scholar
  6. 6.
    Woźnica, A., Kalousis, A., Hilario, M.: Kernels over relational algebra structures. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 588–598. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)Google Scholar
  8. 8.
    Hilario, M., Mitchell, A., Kim, J.H., Bradley, P., Attwood, T.: Classifying protein fingerprints. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 197–208. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Adam Woźnica
    • 1
  • Alexandros Kalousis
    • 1
  • Melanie Hilario
    • 1
  1. 1.Computer Science DepartmentUniversity of GenevaGeneva 4Switzerland

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