Some fundamental interpretability issues in fuzzy modeling

Proceedings - 4th Conference of the European Society for Fuzzy Logic and Technology and 11th French Days on Fuzzy Logic and Applications, EUSFLAT-LFA 2005 Joint Conference

Volumn , Issue , 2005, Pages 100-105

  Mencar, Corrado ^a   Castellano, Giovanna ^a   Fanelli, Anna M ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

Fuzzy modeling; Interpretability

Indexed keywords

FUZZY MODELING; FUZZY MODELS; INTERPRETABILITY; STUDY AND APPLICATIONS;

FUZZY LOGIC;

EID: 47049110031     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. J. Casillas, O. Cordon, F. Herrera, L. Magdalena (eds.), Interpretability Issues in Fuzzy Modeling, Springer, 2003
2. A. Giboin, ML Comprehensibility and KBS explanation: stating the problem collaboratively, Proc. of IJCAI'95 Workshop on Machine Learning and Comprehensibility, 1995, pp. 1-11
3. S. Guillaume, Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on Fuzzy Systems, vol. 9, no. 3, 2001, pp.426-443.
4. P. Hajek, Metamathematics of Fuzzy Logic, Springer, 1998
5. Y. Jin, B. Sendhoff, Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters, vol. 17, 2003, pp. 149-164.
6. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Al-gorithms, Plenum, 1981
7. Y. Kodratoff, Comprehensibility at the junction of Computer Science, Industry, and Cognitive Science, Proc. of IJCAI'95 Workshop on Machine Learning and Comprehensibility, 1995, p. 13
8. R. Krishnapuram, J. Keller, A possibilistc approach to clustering, IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, 1993, pp.98-110
9. C. Mencar, A. Bargiela, G. Castellano, A.M. Fanelli, Interpretable Information Granules with Minkowski FCM, in Proc. of the NAFIPS 2004, pp. 456-461
10. D. Michie, Machine Intelligence and Related Topics. G&B Science Publishers, 1982
11. R.S. Michalski, A theory and methodology of inductive learning, Artificial Intelligence, vol. 20, 1983, pp. 111-161
12. G.A. Miller, The magical number seven, plus or minus two: some limits on our capacity for processing information, Psychological Reviews, vol. 63, 1956, pp. 81-97
13. S. Roychowdhury, An inquiry into the theory of defuzzification, in Granular Computing: an introduction, W. Pedrycz, Ed., Physica-Verlag, 2001, pp. 143-165
14. M.J. Smithson, Words about Uncertainty: Analogies and Contexts, in Computing with Words in Information/Intelligent Systems 1-foundations, L.A. Zadeh, J. Kacprzyk, Eds., Physica-Verlag, 2003, pp. 119-135
15. R. Sun, E. Merrill, T. Peterson, From implicit skills to explicit knowledge: a bottomup model of skill learning, Cognitive Science, vol. 25, no. 2, 2001, pp. 203-244
16. H. Toth, Fuzziness: From epistemic considerations to terminological clarification, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 5, 1997, pp. 481-503
17. J. Valente de Oliveira, Semantic constraints for membership function optimization. IEEE Transactions on Systems, Man and Cybernetics, part A, vol. 29, no. 1, 1999, pp. 128-138.
18. T. van de Merckt, C. Decaestecker, Multiple-Knowledge Representations in Concept Learning. Lecture Notes in Artificial Intelligence, vol. 914, 1995
19. M. van Someron, A perspective on Machine Learning and Comprehensibility from Knowledge Acquisition, Proc. of IJCAI'95Workshop on Machine Learning and Comprehensibility, 1995, pp. 21-25
20. L.A. Zadeh, Soft computing and fuzzy logic, IEEE Software, vol. 11, no. 6, 1994, pp. 48-56