Ordinal Classification with Monotonicity Constraints

  • Tomáš Horváth
  • Peter Vojtáš
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)

Abstract

Classification methods commonly assume unordered class values. In many practical applications – for example grading – there is a natural ordering between class values. Furthermore, some attribute values of classified objects can be ordered, too. The standard approach in this case is to convert the ordered values into a numeric quantity and apply a regression learner to the transformed data. This approach can be used just in case of linear ordering. The proposed method for such a classification lies on the boundary between ordinal classification trees, classification trees with monotonicity constraints and multi-relational classification trees. The advantage of the proposed method is that it is able to handle non-linear ordering on the class and attribute values. For the better understanding, we use a toy example from the semantic web environment – prediction of rules for the user’s evaluation of hotels.

Keywords

Monotone monotonicity constraints classification ordinal data 

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References

  1. 1.
    Džeroski, S., Lavrač, N.: An introduction to inductive logic programming. In: Džeroski, S., Lavrač, N. (eds.) Relational data mining, pp. 48–73. Springer, Heidelberg (2001)Google Scholar
  2. 2.
    Frank, E., Hall, M.: A simple approach to ordinal classification. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS, vol. 2167, pp. 145–156. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Horváth, T., Vojtáš, P.: Fuzzy induction via generalized annotated programs. In: 8th International Conference on Computational Intelligence (Fuzzy Days, Dortmund 2004), Dortmund, Germany, pp. 419–433. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Horváth, T., Sudzina, F., Vojtáš, P.: Mining rules from monotone classification measuring impact of information systems on business competitiveness. In: 6th International Conference on Information Technology for Balanced Automation Systems (BASYS 2004), Wien, Austria, pp. 451–458. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Horváth, T., Krajči, S., Lencses, R., Vojtáš, P.: An ILP model for a graded classification problem. J. Kybernetika 40(3), 317–332 (2004)Google Scholar
  6. 6.
    Leiva, H.A.: MRDTL: A multi-relational decision tree learning algorithm. M.Sc Thesis, Iowa State Univerity, Ames, Iowa (2002)Google Scholar
  7. 7.
    Potharst, R., Feelders, A.J.: Classification Trees for Problems with Monotonicity Constraints. SIGKDD Explorations 4(1), 1–10 (2002)CrossRefGoogle Scholar
  8. 8.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomáš Horváth
    • 1
  • Peter Vojtáš
    • 2
  1. 1.Institute of Computer Science, Faculty of SciencePavol Jozef Šafárik UniversityKošiceSlovakia
  2. 2.Institute of Computer ScienceCzech Academy of SciencesPragueCzech Republic

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