Fuzzy partitioning for clinical DSSs using statistical information transformed into possibility-based knowledge

Knowledge-Based Systems

Volumn 67, Issue , 2014, Pages 1-15

  Pota, Marco ^a   Esposito, Massimo ^a   De Pietro, Giuseppe ^a  

^a ICAR CNR   (Italy)

Author keywords

Clinical DSS; Fuzzy; Partition; Possibility; Probability; Transformation

Indexed keywords

COMPUTER CIRCUITS; DECISION SUPPORT SYSTEMS; FUZZY LOGIC; FUZZY SETS; KNOWLEDGE REPRESENTATION; MEDICAL APPLICATIONS; METADATA; PARTITIONS (BUILDING); PROBABILITY; STATISTICS;

CLINICAL DSS; DECISION SUPPORT SYSTEM (DSSS); FUZZY; LIKELIHOOD FUNCTIONS; POSSIBILITY; STATISTICAL INFORMATION; SYNTHETIC AND REAL DATA; TRANSFORMATION;

PROBABILITY DISTRIBUTIONS;

EID: 84904732185     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.06.021     Document Type: Article

References (57)

1. J. Abonyi, and F. Szeifert Supervised fuzzy clustering for the identification of fuzzy classifiers Pattern Recogn. Lett. 24 2003 2195 2207
2. J. Abonyi Supervised fuzzy clustering based initialization of fuzzy partitions for decision tree induction Soft Comput. Ind. Appl. 75 2010 31 39
3. M.-R. Akbarzadeh-T, and M. Moshtagh-K A hierarchical fuzzy rule-based approach to aphasia diagnosis J. Biomed. Inform. 40 2007 465 475
4. J.M. Alonso, and L. Magdalena Special issue on interpretable fuzzy systems Inform. Sci. 181 20 2011 4331 4339
5. J.M. Alonso, and L. Magdalena HILK++: an interpretability-guided fuzzy modelling methodology for learning readable and comprehensible fuzzy rule-based classifiers Soft Comput. 15 2011 1959 1980
6. J.M. Alonso, C. Castiello, M. Lucarelli, and C. Mencar Modelling interpretable fuzzy rule-based classifiers for medical decision support R. Magdalena, E. Soria, J. Guerrero, J. Gómez-Sanchis, A.J. Serrano, Medical Applications of Intelligent Data Analysis: Research Advancements 2012 IGI Global 255 272
7. M. Antonelli, P. Ducange, B. Lazzerini, and F. Marcelloni Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework Int. J. Approx. Reason. 50 7 2009 1066 1080
8. K. Bache, M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA, 2013. < http://archive.ics.uci.edu/ml >.
9. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Kluwer Academic Publishers Norwell MA, USA
10. T. Bilgic, and I.B. Türksen Measurement of membership functions: Theoretical and empirical work D. Dubois, H. Prade, Handbook of Fuzzy Sets and sysTems Fundamentals of Fuzzy Sets vol. 1 2000 Dordrecht Kluwer 195 227
11. G.E.P. Box, and G.C. Tiao Bayesian Inference in Statistical Analysis 1973 Wiley New York
12. M.-Y. Chen, Establishing interpretable fuzzy models from numeric data, in: Proceedings of the 4th IEEE World Congress on Intelligent Control and Automation, 2002, pp. 1857-1861.
13. E. Coiera Clinical Decision Support Systems, The Guide to Health Informatics second ed. 2003 Arnold London
14. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 3 1995 273 297
15. T.M. Cover, and P.E. Hart Nearest neighbor pattern classification IEEE Trans. Inform. Theory 13 1 1967 21 27
16. A. D'Acierno, G. De Pietro, and M. Esposito Data driven generation of fuzzy systems: an application to breast cancer detection Lect. Notes Comput. Sci., Comput. Intell. Methods Bioinformatics Biostat. 6685 2011 203 214
17. D. Dubois, and H. Prade Fuzzy Sets and Systems: Theory and Applications 1980 Academic Press New York
18. D. Dubois, and H. Prade Fuzzy sets and statistical data Eur. J. Oper. Res. 25 1986 345 356
19. D. Dubois, H. Prade, Fuzzy sets and probability: misunderstandings, bridges and gaps, in: Proceedings of Second IEEE International Conference on Fuzzy Systems, 1993, pp. 1059-1068.
20. D. Dubois, S. Moral, and H. Prade A semantics for possibility theory based on likelihoods J. Math. Anal. Appl. 205 1997 359 380
21. D. Dubois, L. Foulloy, G. Mauris, and H. Prade Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities Reliable Comput. 10 2004 273 297
22. D. Dubois, and H. Prade Gradualness, uncertainty and bipolarity: making sense of fuzzy sets Fuzzy Sets Syst. 192 2012 3 24
23. R.O. Duda, and P.E. Hart Pattern Classification and Scene Analysis 1973 John Wiley & Sons New York
24. C.F. Eick, N. Zeidat, Z. Zhao, Supervised Clustering - Algorithms and Benefits, Technical Report Number UH-CS-05-10, April 25, 2005.
25. P. Fazendeiro, J. Valente De Oliveira, and W. Pedrycz A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller IEEE Trans. Bio-Med. Eng. 54 9 2007 1667 1678
26. M.J. Gacto, R. Alcalà, and F. Herrera Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures Inform. Sci. 181 2011 4340 4360
27. P.Y. Glorennec Algorithmes d'apprentissage pour systèmes d'inférence floue 1999 Editions Hermès Paris
28. J.F. Geer, and G. Klir A mathematical analysis of information-preserving transformations between probabilistic and possibilistic formulations of uncertainty Int. J. Gen. Syst. 20 1992 143 176
29. E. Grossi Medical concepts related to individual risk are better explained with "plausibility" rather than "probability" BMC Cardiovasc. Disord. 5 2005 31 36
30. S. Guillaume Designing fuzzy inference systems from data: an interpretability-oriented review IEEE Trans. Fuzzy Syst. 9 2001 426 443
31. S. Guillaume, and B. Charnomordic A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference from data Studies in Fuzziness and Soft Computing, Interpretability Issues in Fuzzy Modeling vol. 128 2003 Springer 148 175
32. S. Guillaume, and B. Charnomordic Learning interpretable fuzzy inference systems with FisPro Inform. Sci. 181 2011 4409 4427
33. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA data mining software: an update SIGKDD Explor. 11 1 2009 10 18
34. G.J. Klir A principle of uncertainty and information invariance Int. J. Gen. Syst. 17 1990 249 275
35. K. Loquin, and O. Strauss Fuzzy histograms and density estimation J. Lawry, E. Miranda, A. Bugarin, S. Li, M. Gil, P. Grzegorzewski, O. Hyrniewicz, Soft Methods for Integrated Uncertainty Modelling vol. 37 2006 Spinger-Verlag Berlin Heidelberg 45 52
36. J. Ma, J. Lu, and G. Zhang Decider: a fuzzy multi-criteria group decision support system Knowl.-Based Syst. 23 2010 23 31
37. J. Ma, J. Lu, and G. Zhang A three-level-similarity measuring method of participant opinions in multiple-criteria group decision supports Decis. Support Syst. 59 2014 74 83
38. J.B. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of 5-th Symposium on Mathematical Statistics and Probability, vol. 1, 1967, pp. 281-297.
39. C. Mencar, and A.M. Fanelli Interpretability constraints for fuzzy information granulation Inform. Sci. 178 24 2008 4585 4618
40. E. Parzen On estimation of a probability density function and mode Ann. Math. Stat. 33 3 1962 1065 1076
41. W. Pedrycz Fuzzy equalization in the construction of fuzzy sets Fuzzy Sets Syst. 64 1994 21 30
42. M. Pota, M. Esposito, G. De Pietro, Transformation of probability distribution into a fuzzy set interpretable with likelihood view, in: Proceedings of IEEE 11th International Conference on Hybrid Intelligent Systems, 2011, pp. 91-96.
43. M. Pota, M. Esposito, G. De Pietro, From likelihood uncertainty to fuzzyness: a possibility-based approach for building clinical DSSs, in: Proceedings of The 7th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, vol. 7209, 2012, pp 369-380.
44. M. Pota, M. Esposito, and G. De Pietro Transforming probability distributions into membership functions of fuzzy classes: a hypothesis test approach Fuzzy Sets Syst. 233 2013 52 73 10.1016/j.fss.2013.03.013
45. J.R. Quinlan Induction on decision trees Mach. Learn. 1 1986 81 106
46. J.R. Quinlan C4.5 Programs for Machine Learning 1993 Morgan Kaufmann Publisher San Mateo, CA
47. M. Rosenblatt Remarks on some nonparametric estimates of a density function Ann. Math. Stat. 27 3 1956 832 837
48. T.E. Rothenfluh, K. Bögl, and K.-P. Adlassnig Representation and acquisition of knowledge for a fuzzy medical consultation system P.S. Szczepaniak, P.J.G. Lisboa, J. Kacprzyk, Studies in Fuzziness and Soft Computing, Fuzzy Systems in Medicine vol. 41 2000 Physica-Verlag HD 636 651
49. G. Shafer A Mathematical Theory of Evidence 1976 Princeton University Press NJ
50. J. Valente de Oliveira Semantic constraints for membership function optimization IEEE Trans. Syst., Man, Cybern. - Part A: Syst. Hum. 29 1 1999 128 138
51. L.X. Wang, and J.M. Mendel Generating fuzzy rules by learning from examples IEEE Trans. Syst. Man Cybern. 22 1992 1414 1427
52. P.J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD thesis, Harvard University, 1974.
53. R. Yager, and V. Kreinovich Entropy conserving probability transforms and the entailment principle Fuzzy Sets Syst. 158 12 2007 1397 1405
54. K. Yamada, Probability-possibility transformation based on evidence theory, in: Proceedings of Joint 9th IFSA World Congress and 20th NAIPS International Conference, 2001, pp. 70-75.
55. Y. Yuan, and M.J. Shaw Induction of fuzzy decision trees Fuzzy Sets Syst. 69 1995 125 139
56. L. Zadeh Fuzzy sets Inform. Contr. 8 1965 338 353
57. L. Zadeh Fuzzy sets as a basis for a theory of possibility Fuzzy Sets Syst. 1 1978 3 28