Multiple Instance Learning with Genetic Programming for Web Mining

  • A. Zafra
  • S. Ventura
  • E. Herrera-Viedma
  • C. Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process.


Multiple Instance Multiple Instance Learn Lazy Learning Knowledge Discovery Process Grammar Base Genetic 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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  1. 1.
    Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artifical Intelligence 89(1-2), 31–71 (1997)zbMATHCrossRefGoogle Scholar
  2. 2.
    Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). MIT Press, Cambridge (2006)Google Scholar
  3. 3.
    Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: ICML’97: Proceedings of 14th International Conference on Machine Learning), Nashville, US, pp. 143–151. Morgan Kaufmann, San Francisco (1997), Google Scholar
  4. 4.
    Zhou, Z.-H., Jiang, K., Li, M.: Multi-instance learning based web mining. Applied Intelligence 22(2), 135–147 (2005)CrossRefGoogle Scholar
  5. 5.
    Auer, P.: On learning from multi-instance examples: Empirical evaluation of a theoretical approach. In: ICML’97: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 21–29. Morgan Kaufmann, San Francisco (1997)Google Scholar
  6. 6.
    Maron, O.: A framework for multiple-instance learning. In: NIPS’97: Proceedings of Neural Information Processing System 10, Denver, Colorado, United States, pp. 570–576. MIT Press, Cambridge (1997)Google Scholar
  7. 7.
    Zhang, Q., Goldman, S.: EM-DD: An improved multiple-instance learning technique. In: NIPS’01: Proceedings of Neural Information Processing System 14 (2001),
  8. 8.
    Long, P.M., Tan, L.: PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples. Machine Learning 30(1), 7–21 (1998)zbMATHCrossRefGoogle Scholar
  9. 9.
    Wang, J., Zucker, J.-D.: Solving the multiple-instance problem: A lazy learning approach. In: ICML’00: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 1119–1126. Morgan Kaufmann, San Francisco (2000)Google Scholar
  10. 10.
    Chevaleyre, Y., Zucker, J.-D.: Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem. In: Stroulia, E., Matwin, S. (eds.) Canadian AI 2001. LNCS (LNAI), vol. 2056, pp. 204–214. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Ruffo, G.: Learning single and multiple instance decision tree for computer security applications. PhD thesis, Department of Computer Science. University of Turin, Torino, Italy (2000)Google Scholar
  12. 12.
    Zhang, M.-L., Zhou, Z.-H.: Ensembles of multi-instance neural networks. In: Intelligent information processing II. IFIP International Federation for Information Processing, vol. 163, pp. 471–474. Springer, Boston (2005)CrossRefGoogle Scholar
  13. 13.
    Zhang, M.-L., Zhou, Z.-H.: Adapting rbf neural networks to multi-instance learning. Neural Processing Letters 23(1), 1–26 (2006)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS’02: Proceedings of Neural Information Processing System, pp. 561–568 (2002)Google Scholar
  15. 15.
    Tao, Q., Scott, S., Vinodchandran, N.V., Osugi, T.T.: SVM-based generalized multiple-instance learning via approximate box counting. In: ICML’04: Proceedings of the twenty-first international conference on Machine learning, Banff, Alberta, Canada, pp. 799–806. ACM Press, New York (2004), doi:10.1145/1015330.1015405Google Scholar
  16. 16.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  17. 17.
    Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. IEEE Trans. Evolutionary Computation 8(2), 183–196 (2004)CrossRefGoogle Scholar
  18. 18.
    Zhang, M., Smart, W.: Using gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recognition Letters 27(11), 1266–1274 (2006), doi:10.1016/j.patrec.2005.07.024CrossRefGoogle Scholar
  19. 19.
    Ventura, S., Romero, C., Zafra, A., Delgado, J.A., Hervás, C.: JCLEC: A java framework for evolutionary computation soft computing. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12(4), 381–392 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • A. Zafra
    • 1
  • S. Ventura
    • 2
  • E. Herrera-Viedma
    • 1
  • C. Romero
    • 2
  1. 1.Department of Computer Science and Artificial Intelligence. University of Granada 
  2. 2.Department of Computer Science and Numerical Analysis. University of Córdoba 

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