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A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning

  • Amelia Zafra
  • Sebastián Ventura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

Abstract

This paper develops a first comparative study of multi- objective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective evolutionary algorithms. Thus, we design and implement SPG3P-MI based on the Strength Pareto Evolutionary Algorithm, NSG3P-MI based on the Non-dominated Sorting Genetic Algorithm and MOGLG3P-MI based on the Multi-objective genetic local search. These approaches are tested with different MIL applications and compared to a previous single-objective grammar-guided genetic programming proposal. The results demonstrate the excellent performance of multi-objective approaches in achieving accurate models and their ability to generate comprehensive rules in the knowledgable discovery process.

Keywords

Genetic Programming Pareto Optimal Front Strength Pareto Evolutionary Algorithm Multiple Instance Learn Drug Activity Prediction 
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.
    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)CrossRefzbMATHGoogle Scholar
  2. 2.
    Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS 2002: Proceedings of Neural Information Processing System, Vancouver, Canada, pp. 561–568 (2002)Google Scholar
  3. 3.
    Pao, H.T., Chuang, S.C., Xu, Y.Y., Fu, H.: An EM based multiple instance learning method for image classification. Expert Systems with Applications 35(3), 1468–1472 (2008)CrossRefGoogle Scholar
  4. 4.
    Yang, C., Dong, M., Fotouhi, F.: Region based image annotation through multiple-instance learning. In: Multimedia 2005: Proceedings of the 13th Annual ACM International Conference on Multimedia, New York, USA, pp. 435–438 (2005)Google Scholar
  5. 5.
    Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: NIPS 1997: Proceedings of Neural Information Processing System 10, Denver, Colorado, USA, pp. 570–576 (1997)Google Scholar
  6. 6.
    Zhou, Z.H., Zhang, M.L.: Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems 11(2), 155–170 (2007)CrossRefGoogle Scholar
  7. 7.
    Zafra, A., Ventura, S., Romero, C., Herrera-Viedma, E.: Multiple instance learning with genetic programming for web mining. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 919–927. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Gloriastrasse 35 (2001)Google Scholar
  10. 10.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Jaszkiewicz, A., Kominek, P.: Genetic local search with distance preserving recombination operator for a vehicle routing problem. European Journal of Operational Research 151(2), 352–364 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Zafra, A., Ventura, S.: G3P-MI: A genetic programming algorithm for multiple instance learning. In: Information Science. Elsevier, Amsterdam (submitted)Google Scholar
  13. 13.
    Whigham, P.A.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, California, USA, pp. 33–41 (1995)Google Scholar
  14. 14.
    Shukla, P.K., Deb, K.: On finding multiple pareto-optimal solutions using classical and evolutionary generating methods. European Journal of Operational Research 181(3), 1630–1652 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Parrott, D., Xiaodong, L., Ciesielski, V.: Multi-objective techniques in genetic programming for evolving classifiers. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1141–1148 (September 2005)Google Scholar
  16. 16.
    Mugambi, E.M., Hunter, A.: Multi-objective genetic programming optimization of decision trees for classifying medical data. In: KES 2003: Knowledge-Based Intelligent Information and Engineering Systems, pp. 293–299 (2003)Google Scholar
  17. 17.
    Wiens, T.S., Dale, B.C., Boyce, M.S., Kershaw, P.G.: Three way k-fold cross-validation of resource selection functions. Ecological Modelling 212(3-4), 244–255 (2008)CrossRefGoogle Scholar
  18. 18.
    Ventura, S., Romero, C., Zafra, A., Delgado, J.A., Hervás, C.: JCLEC: A java framework for evolutionary computation soft computing. Soft Computing 12(4), 381–392 (2008)CrossRefGoogle Scholar
  19. 19.
    Coello, C.A., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic and Evolutionary Computation, 2nd edn. Springer, New York (2007)Google Scholar
  20. 20.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Amelia Zafra
    • 1
  • Sebastián Ventura
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaSpain

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