Fuzzy hypotheses testing in the framework of fuzzy logic

Fuzzy Sets and Systems

Volumn 145, Issue 2, 2004, Pages 229-252

  Holeňa, Martin ^a  

^a INSTITUTE OF COMPUTER SCIENCE   (Czech Republic)

Author keywords

Basic fuzzy logic; Fuzzy predicate calculus; Fuzzy statistics and data analysis; Generalized quantifiers; Method Guha; Non classical logics; Vague hypotheses; Vague significance level

Indexed keywords

COMPUTATION THEORY; DATA MINING; DATA REDUCTION; PROBABILITY DISTRIBUTIONS; SET THEORY; STATISTICAL METHODS;

BASIC FUZZY LOGIC; FUZZY PREDICATE CALCULUS; FUZZY STATISTICS; GENERALIZED QUANTIFIERS; METHOD GUHA; NON CLASSICAL LOGICS; VAGUE HYPOTHESIS; VAGUE SIGNIFICANCE LEVEL;

FUZZY SETS;

EID: 2942560785     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(03)00208-2     Document Type: Article

References (32)

1. Alexander J.A., Mozer M.C. Template-based procedures for neural network interpretation. Neural Networks. 12:1999;479-498.
2. Arnold B.F. Statistical tests optimally meeting certain fuzzy requirements on the power function and on the sample size. Fuzzy Sets and Systems. 75:1995;365-372.
3. Arnold B.F. An approach to fuzzy hypotheses testing. Metrika. 42:1996;119-126.
4. Arnold B.F. Testing fuzzy hypotheses with crisp data. Fuzzy Sets and Systems. 94:1998;232-333.
5. Casals M.R. Bayesian testing of fuzzy parametric hypotheses from fuzzy information. Rech. Oper./Oper. Res. 27:1993;189-199.
6. De Raedt L. Interactive Theory Revision: An Inductive Logic Programming Approach. 1992;Academic Press, London.
7. Delgado M., Verdegay J.L., Vila M.A. Testing fuzzy hypotheses. a bayesian approach. Gupta M.M., Kandel A., Bandler W., Kiszka J.B. Approximate Reasoning in Expert Systems. 1985;307-316 North-Holland, Amsterdam.
8. Džeroski S. Inductive logic programming and knowledge discovery in databases. Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;117-152 AAAI Press, Menlo Park.
9. Grabisch M., Nguyen H.T., Walker E.A. Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. 1995;Kluwer Academic Publishers, Dordrecht.
10. Grzegorzewski P., Hryniewicz O. Testing statistical hypotheses in fuzzy environment. Mathware Soft Comput. 4:1997;203-217.
11. Hájek P. Metamathematics of Fuzzy Logic. 1998;Kluwer Academic Publishers, Dordrecht.
12. Hájek P., Havránek T. On generating of inductive hypotheses. Internat. J. Man-Mach. Stud. 9:1977;415-438.
13. Hájek P., Havránek T. Mechanizing Hypothesis Formation. 1978;Springer, Berlin.
14. Hájek P., Sochorová A., Zvárová J. Guha for personal computers. Comput. Statist. Data Anal. 19:1995;149-153.
15. Harmancová D., Holeňa M., Sochorová A. Overview of the Guha method for automating knowledge discovery in statistical data sets. Noirhomme-Fraiture M. Knowledge Extraction and Symbolic Data Analysis. 1999;65-77 Eurostat, Luxembourg.
16. Havránek T. Statistical quantifiers in observational calculi. an application in Guha methods Theory Decision. 6:1975;213-230.
17. Holeňa M. Exploratory data processing using a fuzzy generalization of the Guha approach. Baldwin J.F. Fuzzy Logic. 1996;213-229 Wiley, New York.
18. Holeňa M. Fuzzy hypotheses for Guha implications. Fuzzy Sets and Systems. 98:1998;101-125.
19. M. Holeňa, A fuzzy logic framework for testing vague hypotheses with empirical data in: Proc. Fourth Internat. ICSC Symp. on Soft Computing and Intelligent Systems for Industry, ICSC Academic Press, Sliedrecht, 2001, pp. 401-407.
20. Holeňa M. A fuzzy logic generalization of a data mining approach. Neural Network World. 11:2001;595-610.
21. M. Holeňa, Mining rules from empirical data with an ecological application, Tech. Report, Brandenburg University of Technology, Cottbus, 2002, ISBN 3-934934-07-2, 62pp.
22. Holeňa M., Sochorová A., Zvárová J. Increasing the diversity of medical data mining through distributed object technology. Kokol P., Zupan B., Stare J., Premik M., Engelbrecht R. Medical Informatics Europe '99. 1999;442-447 IOS Press, Amsterdam.
23. Klösgen W. Explora: a multipattern and multistrategy discovery assistant. Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;249-272 AAAI Press, Menlo Park.
24. Mareš M., Mesiar R. Calculation over verbal quantities. Zadeh L.A., Kacprzyk J. Computing with Words in Information/Intelligent Systems. 1999;409-427 Physica-Verlag, Heidelberg.
25. Muggleton S. Inductive Logic Programming. 1992;Academic Press, London.
26. Narayanan A. Revisable knowledge discovery in databases. Internat. J. Intell. Systems. 11:1996;75-96.
27. Rauch J. Logical calculi for knowledge discovery in databases. Komorowski J., Zytkov J.M. Principles of Data Mining and Knowledge Discovery. 1997;47-57 Springer, Berlin.
28. Römer C., Kandel A. Statistical tests for fuzzy data. Fuzzy Sets and Systems. 72:1995;1-26.
29. Saade J.J., Schwarzlander H. Fuzzy hypotheses testing with hybrid data. Fuzzy Sets and Systems. 35:1990;213-217.
30. Shen W.M., Ong K.L., Mitbander B., Zamiolo C. Metaqueries for data mining. Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;375-398 AAAI Press, Menlo Park.
31. Watanabe N., Imaizumi T. A fuzzy statistical test of fuzzy hypotheses. Fuzzy Sets and Systems. 53:1993;167-178.
32. Zembowicz R., Zytkov J. From contingency tables to various forms of knowledge in databases. Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;329-352 AAAI Press, Menlo Park.