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Learning and Evaluating Possibilistic Decision Trees using Information Affinity

Authors: Zied Elouedi, Ilyes Jenhani, Salem Benferhat


This paper investigates the issue of building decision trees from data with imprecise class values where imprecision is encoded in the form of possibility distributions. The Information Affinity similarity measure is introduced into the well-known gain ratio criterion in order to assess the homogeneity of a set of possibility distributions representing instances-s classes belonging to a given training partition. For the experimental study, we proposed an information affinity based performance criterion which we have used in order to show the performance of the approach on well-known benchmarks.

Keywords: Possibility Theory, Decision trees, Data mining from uncertain data

Digital Object Identifier (DOI):

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[1] N. Ben Amor, S. Benferhat, Z. Elouedi: Qualitative classification with possibilistic decision trees, (IPMU-04), Perugia, Italy, 2004.
[2] N. Ben Amor, S. Benferhat, Z. Elouedi: Qualitative classification and evaluation in possibilistic decision trees, (FUZZ-IEEE-04), Hungary, 2004, 653-657.
[3] C. Borgelt, J. Gebhardt, R. Kruse: Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data. (EUFIT-96), 1996, 1556-1560.
[4] T. Denoeux and M. S. Bjanger: Induction of decision trees from partially classified data. SMC-00, Nashville, TN, 2000, 2923-2928.
[5] T. Denoeux and L. M. Zouhal. Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets and Systems, 122(3), 2001, 47-62.
[6] D. Dubois and H. Prade: Possibility theory: An approach to computerized processing of uncertainty, Plenum Press, New York, 1988.
[7] Z. Elouedi, K. Mellouli and P. Smets. Belief decision trees: Theoretical foundations. International Journal of Approximate Reasoning, 28, 2001, 91-124.
[8] E. H├╝llermeier. Possibilistic Induction in decision tree learning. ECML-02, Helsinki, Finland, 2002, 173-184.
[9] I. Jenhani, N. Ben Amor, Z. Elouedi, S. Benferhat and K. Mellouli: Information Affinity: a new similarity measure for possibilistic uncertain information, ECSQARU-07, Hammamet, Tunisia, 2007, 840-852.
[10] I. Jenhani, N. Ben Amor, Z. Elouedi: Decision Trees as Possibilistic Classifiers, International Journal of Approximate Reasoning, 48(3), 2008, 784-807.
[11] C. Z. Janikow. Fuzzy decision trees: issues and methods. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 28(1),1998, 1-14.
[12] C. Marsala: Apprentissage inductif en présence de données imprécises: construction et utilisation d-arbres de décision flous, PhD thesis, University P. et M. Curie, Paris, France, 1998.
[13] A. Motro: Sources of Uncertainty, Imprecision and Inconsistency in Information Systems. In Uncertainty Management in Information Systems: From Needs to Solutions, 1996, 9-34.
[14] P. M. Murphy, D.W. Aha: UCI repository of machine learning databases, 1996.
[15] J. R. Quinlan: Induction of decision trees, Machine Learning, 1, 1986, 81-106.
[16] J. R. Quinlan: C4.5: Programs for machine learning, Morgan Kaufmann, 1993.
[17] Y. Yuan, M.J. Shaw: Induction of fuzzy decision trees, Fuzzy Sets and Systems, 69, 1995, 125-139.
[18] L. A. Zadeh: Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets ans Systems, 1, 1978, 3-28.