Exploiting Propositionalization Based on Random Relational Rules for Semi-supervised Learning

  • Grant Anderson
  • Bernhard Pfahringer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)


In this paper we investigate an approach to semi-supervised learning based on randomized propositionalization, which allows for applying standard propositional classification algorithms like support vector machines to multi-relational data. Randomization based on random relational rules can work both with and without a class attribute and can therefore be applied simultaneously to both the labeled and the unlabeled portion of the data present in semi-supervised learning.

An empirical investigation compares semi-supervised propositionalization to standard propositionalization using just the labeled data portion, as well as to a variant that also just uses the labeled data portion but includes the label information in an attempt to improve the resulting propositionalization. Preliminary experimental results indicate that propositionalization generated on the full dataset, i.e. the semi- supervised approach, tends to outperform the other two more standard approaches.


semi-supervised propositionalization randomization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, G., Pfahringer, B.: Clustering Relational Data based on Randomized Propositionalization. In: Proceedings International Conference on Inductive Logic Programming 2007 (ILP 2007). Springer, Heidelberg (2007)Google Scholar
  2. 2.
    Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    de Castro Dutra, I., Page, D., Santos Costa, V., Shavlik, J.: An Empirical Evaluation of Bagging in Inductive Logic Programming. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)Google Scholar
  5. 5.
    Dietterich, T., Lathrop, R., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)CrossRefzbMATHGoogle Scholar
  6. 6.
    Džeroski, S., Schulze-Kremer, S., Heidtke, K.R., Siems, K., Wettschereck, D.: Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra. In: Proc. 6th Int. Workshop on Inductive Logic Programming, pp. 41–54 (1996)Google Scholar
  7. 7.
    Freund, Y., Mason, L.: The Alternating Decision Tree Learning Algorithm. In: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999). Morgan Kaufmann, San Francisco (1999)Google Scholar
  8. 8.
    Giordana, A., Saitta, L.: Phase Transitions in Relational Learning. Machine Learning 41(2), 217–251 (2000)CrossRefzbMATHGoogle Scholar
  9. 9.
    Horváth, T., Wrobel, S., Bohnebeck, U.: Relational Instance-Based Learning with Lists and Terms. Machine Learning 43, 53–80 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    King, R.D., Srinivasan, A., Dehaspe, L.: Warmr: A Data Mining Tool for Chemical Data. Journal of Computer Aided Molecular Design 15, 173–181 (2001)CrossRefGoogle Scholar
  11. 11.
    Kleinberg, E.M.: On the Algorithmic Implementation of Stochastic Discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(5), 473–490 (2000)CrossRefGoogle Scholar
  12. 12.
    Kramer, S., Lavrac, N., Flach, P.: Propositionalization Approaches to Relational Data Mining. Relational Data Mining. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Krogel, S., Scheffer, T.: Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics. Machine Learning 57, 61–81 (2004)CrossRefzbMATHGoogle Scholar
  14. 14.
    Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)Google Scholar
  15. 15.
    Pfahringer, B., Leschi, C., Reutemann, P.: Scaling Up Semi-supervised Learning: An Efficient and Effective LLGC Variant. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 236–247. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Platt, J.: Machines using Sequential Minimal Optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1998)Google Scholar
  17. 17.
    Ping, L., Zhe, W., Chunguang, Z.: A Spectrum-Based Support Vector Algorithm for Relational Data Semi-supervised Classification. In: Proceedings of the 13th International Conference on Neural Information Processing, pp. 801–810 (2006)Google Scholar
  18. 18.
    Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)Google Scholar
  19. 19.
    Srinivasan, A., Muggleton, S., King, R.D., Sternberg, M.J.E.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Proceedings of the Fourth Inductive Logic Programming Workshop (1994)Google Scholar
  20. 20.
    Srinivasan, A., Muggleton, S., King, R.D., Sternberg, M.J.E.: Carcinogenesis Predictions Using ILP. In: Proceedings of the 7th International Workshop on Inductive Logic Programming (1997), pp. 273–887 (1994)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    Zelezny, F., Lavrac, N.: Propositionalization-Based Relational Subgroup Discovery with RSD. Machine Learning 62(1-2), 33–63 (2006)CrossRefGoogle Scholar
  23. 23.
    Zhou, D., Huang, J., Schölkopf, B.: Learning from labeled and unlabeled data on a directed graph. In: Proc. of the 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, August 2005, pp. 1041–1048 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Grant Anderson
    • 1
  • Bernhard Pfahringer
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

Personalised recommendations