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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
Horváth, T., Krajči, S., Lencses, R., Vojtáš, P.: An ILP model for a graded classification problem. J. Kybernetika 40(3), 317–332 (2004)
Leiva, H.A.: MRDTL: A multi-relational decision tree learning algorithm. M.Sc Thesis, Iowa State Univerity, Ames, Iowa (2002)
Potharst, R., Feelders, A.J.: Classification Trees for Problems with Monotonicity Constraints. SIGKDD Explorations 4(1), 1–10 (2002)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Horváth, T., Vojtáš, P. (2006). Ordinal Classification with Monotonicity Constraints. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_17
Download citation
DOI: https://doi.org/10.1007/11790853_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
Online ISBN: 978-3-540-36037-7
eBook Packages: Computer ScienceComputer Science (R0)