Credibility Coefficients for Objects of Rough Sets

• Autorzy: Podraza R. , Dominik A.
• Języki publikacji: EN

Abstrakty

EN

In this paper focus is set on data reliability. We propose a few methods, which calculate credibility coefficients for objects stored in decision tables. Credibility coefficient of object is a measure of its similarity with respect to the rest of the objects in the considered decision table. It can be very useful in detecting either corrupted data or abnormal and distinctive situations. It is assumed that the proper data appear in majority and can be separated from improper data by exploring mutual resemblance. The proposed methods take advantage of well known and widely used data mining technique - rough sets.

Słowa kluczowe

EN

data reliability; credibility coefficient; information system; decision table; rough sets; artificial intelligence

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2006
• Tom: Vol. 1(7)
• Strony: 93--104
• Opis fizyczny: Bibliogr. 13 poz., tab.

Twórcy

autor

Podraza R.

• Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

autor

Dominik A.

• Institute of Radioelectronics, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

Bibliografia

• 1. Podraza R., Podraza W., (2002). Rough Set System with Data Elimination, Proceedings of the 2002 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS’2002), Las Vegas, Nevada, USA, 493-499.
• 2. Podraza R., Dominik A., Walkiewicz M., (2003). Decision Support System for Medical Applications, Proceedings of the IASTED International Conference on Applied Simulations and Modelling (ASM’2003), Marbella, Spain, 329-334.
• 3. Podraza R., Dominik A., Walkiewicz M., (2005). ARES Rough Set Exploration System, Proceedings of The Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC’2005), Regina, Canada, volume II, Lecture Notes in Artificial Intelligence 3642, 29-38.
• 4. Dominik A., (2004). Data Analysis Based on Rough Set Theory, M.Sc. Thesis, Institute of Computer Science, Warsaw University in Technology, Warsaw, Poland, (in Polish).
• 5. Walkiewicz M., (2004). Problem of Data Reliability in Decision Tables, M.Sc. Thesis, Institute of Computer Science, Warsaw University in Technology, Warsaw, Poland, (in Polish).
• 6. Pawlak Z., (1984). Rough Classification, International Journal of Man-Machine Studies, volume 20, 469-483.
• 7. Pawlak Z., (1982). Rough Sets, International Journal of Computer-Information Sciences, volume 11, 341-356.
• 8. Pawlak Z., (1985). Decision Tables and Decision Algorithms, International Bulletin of the Polish Academy of Sciences, Technical Sciences, volume 33, 487-494.
• 9. Pawlak Z., (1991). Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer, Dordrecht.
• 10. UCI Repository of Machine Learning Databases and Domain Theories, http://www.ies.uci.edu.
• 11. Podraza W., (2000). The method of Reduction of Rough Sets Theory Results Misinterpretation in Medicine, Proceedings of Second Symposium on Modelling and Measurements in Medicine, Krynica, Poland, (in Polish).
• 12. Podraza R., Dominik A., Walkiewicz M., (2005). Application of ARES Rough Set Exploration System for Data Analysis, Conference Computer Science — Research and Applications, Kazimierz Dolny, Poland, Annales Universitatis Mariae Curie-Skiodowska, Sectio AI Informatica, volume III, (2005).
• 13. Podraza W., Kordek A., Podraza R., (2004). Rough Set Method in the Diagnosis of Neonatal Infection, 6th World Congress on Trauma, Shock, Inflamation and Sepsis — Pathophysiology, Immune Consequences and Therapy, LMU Munich, Germany.