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Feature Discovery with Type Extension Trees

  • Paolo Frasconi
  • Manfred Jaeger
  • Andrea Passerini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5194)

Abstract

We are interested in learning complex combinatorial features from relational data. We rely on an expressive and general representation language whose semantics allows us to express many features that have been used in different statistical relational learning settings. To avoid expensive exhaustive search over the space of relational features, we introduce a heuristic search algorithm guided by a generalized relational notion of information gain and a discriminant function. The algorithm succesfully finds interesting and interpretable features on artificial and real-world relational learning problems.

Keywords

Discriminant Function Class Label Feature Discovery Metal Binding Site Inductive Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Van Assche, A., Vens, C., Blockeel, H., Dzeroski, S.: First order random forests: Learning relational classifiers with complex aggregates. Machine Learning 64, 149–182 (2006)zbMATHCrossRefGoogle Scholar
  2. 2.
    Blockeel, H., De Raedt, L.: Lookahead and discretization in ilp. In: Proc. of the 7th Int. Workshop on ILP, pp. 77–84 (1997)Google Scholar
  3. 3.
    Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artificial Intelligence (101), 285–297 (1998)Google Scholar
  4. 4.
    Castillo, L.P., Wrobel, S.: A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning. In: Proc. of ICML 2004 (2004)Google Scholar
  5. 5.
    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proc. of IJCAI 1999 (1999)Google Scholar
  6. 6.
    Hulo, N., Sigrist, C.J.A., Le Saux, V., Langendijk-Genevaux, P.S., Bordoli, L., Gattiker, A., De Castro, E., Bucher, P., Bairoch, A.: Recent improvements to the prosite database. Nucleic Acids Research 32(Database-Issue), 134–137 (2004)CrossRefGoogle Scholar
  7. 7.
    Jaeger, M.: Type extension trees: a unified framework for relational feature construction. In: Proceedings of Mining and Learning with Graphs (MLG 2006) (2006)Google Scholar
  8. 8.
    Jensen, D., Neville, J., Hay, M.: Avoiding bias when aggregating relational data with degree disparity. In: Proc. of ICML 2003 (2003)Google Scholar
  9. 9.
    Knobbe, A.J., Siebes, A., van der Wallen, D.: Multi-relational decision tree induction. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 378–383. Springer, Heidelberg (1999)Google Scholar
  10. 10.
    Neville, J., Jensen, D.: Collective classification with relational dependency networks. In: Proc. of 2nd Int. Workshop on Multi-Relational Data Mining, pp. 77–91 (2003)Google Scholar
  11. 11.
    Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: Proceedings of SIGKDDD 2003 (2003)Google Scholar
  12. 12.
    Passerini, A., Punta, M., Ceroni, A., Rost, B., Frasconi, P.: Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks. Proteins 65(2), 305–316 (2006)CrossRefGoogle Scholar
  13. 13.
    Perlich, C., Provost, F.: Aggregation-based featrue invention and relational concept classes. In: Proc. of SIGKDD 2003 (2003)Google Scholar
  14. 14.
    Popescul, A., Ungar, L.H.: Feature generation and selection in multi-relational statistical learning. In: Getoor, L., Taskar, B. (eds.) Statistical Relational Learning. MIT Press, Cambridge (2007)Google Scholar
  15. 15.
    Singla, P., Domingos, P.: Entity resolution with markov logic. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065. Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148(3), 839–843 (1983)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paolo Frasconi
    • 2
  • Manfred Jaeger
    • 1
  • Andrea Passerini
    • 2
  1. 1.Department for Computer ScienceAalborg UniversityDenmark
  2. 2.Dipartimento di Sistemi e InformaticaUniversitá degli Studi di FirenzeItaly

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