On the use of clustering in possibilistic decision tree induction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5590 LNAI, Issue , 2009, Pages 505-517

  Jenhani, Ilyes ^a,b   Benferhat, Salem ^b   Elouedi, Zied ^a  

^a UNIVERSITÉ DE TUNIS   (Tunisia)

^b UNIVERSITÉ D'ARTOIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRIBUTE SELECTION; C4.5 ALGORITHM; DECISION TREE CLASSIFIERS; DECISION TREE INDUCTION; LABELED TRAINING DATA; POSSIBILISTIC; POSSIBILITY DISTRIBUTIONS; POSSIBILITY THEORY FRAMEWORK;

LABELS;

DECISION TREES;

ALGORITHMS; DATA PROCESSING; DECISION MAKING; PROBABILITY; PROBLEM SOLVING;

EID: 69049115306     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02906-6_44     Document Type: Conference Paper

References (23)

1. Ben Amor, N., Benferhat, S., Elouedi, Z.: Qualitative classification and evaluation in possibilistic decision trees. In: FUZZ-IEEE 2004, pp. 653-657 (2004)
2. Blockeel, H., Page, D., Srinivasan, A.: Multi-instance tree learning. In: Proceedings of the 22nd international conference on Machine learning, Bonn, Germany, pp. 57-64 (2005)
3. Borgelt, C., Gebhardt, J., Kruse, R.: Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data. In: EUFIT 1996, pp. 1556-1560 (1996)
4. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth & Brooks, Monterey (1984)
5. Dasarathy, B.V.: Nearest Neighbour(NN) norms: NN pattern classification techniques. IEEE Computer sociaty Press, Los Alamitos (1991)
6. Dubois, D., Prade, H.: Possibility theory. An approach to computerized processing of uncertainty. Plenum Press, New York (1988)
7. Hüllermeier, E.: Possibilistic Induction in decision tree learning. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS, vol. 2430, pp. 173-184. Springer, Heidelberg (2002)
8. Hüllermeier, E., Beringer, J.: Learning from Ambiguously Labeled Examples. In: Intelligent Data Analysis, pp. 168-179 (2005)
9. Friedman, J.H., Kohavi, R., Yun, Y.: Lazy decision trees. In: Proceedings of the 13th National Conference on Artificial Intelligence, pp. 717-724 (1996)
10. Jenhani, I., Elouedi, Z., Ben Amor, N., Mellouli, K.: Qualitative inference in possibilistic option decision trees. In: Godo, L. (ed.) ECSQARU 2005. LNCS, vol. 3571, pp. 944-955. Springer, Heidelberg (2005)
11. Jenhani, I., Ben Amor, N., Elouedi, Z., Benferhat, S., Mellouli, K.: Information Affinity: a new similarity measure for possibilistic uncertain information. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS, vol. 4724, pp. 840-852. Springer, Heidelberg (2007)
12. Jenhani, I., Ben Amor, N., Elouedi, Z.: Decision Trees as Possibilistic Classifiers. International Journal of Approximate Reasoning 48(3), 784-807 (2008)
13. Jenhani, I., Ben Amor, N., Benferhat, S., Elouedi, Z.: SIM-PDT: A Similarity Based Possibilistic Decision Tree Approach. In: Hartmann, S., Kern-Isberner, G. (eds.) FoIKS 2008. LNCS, vol. 4932, pp. 348-364. Springer, Heidelberg (2008)
14. Jenhani, I., Benferhat, S., Elouedi, Z.: Properties Analysis of Inconsistency-based Possibilistic Similarity Measures. In: Proceedings of IPMU 2008, pp. 173-180 (2008)
15. Jenhani, I., Benferhat, S., Elouedi, Z.: Learning and evaluating possibilistic decision trees using information affinity. International Journal of Computer Systems Science and Engineering 4(3), 206-112 (2008)
16. Jensen, F.V.: Introduction to Bayesian networks. UCL Press (1996)
17. Johnson, S.C.: Hierarchical Clustering Schemes. Psychometrika 2, 241-254 (1967)
18. Kruse, R., Gebhardt, J., Klawonn, F.: Foundations of Fuzzy Systems. John Wiley and Sons, Chichester (1994)
19. Moulin, H.: Axioms for cooperative decision-making. Cambridge Univ. Press, Cambridge (1988)
20. Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases (1996)
21. Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81-106 (1986)
22. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
23. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets ans Systems 1, 3-28 (1978)