Application of Credibility Coefficients Based on Decision Rules

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

Abstrakty

EN

Credibility coefficients reflect similarity of objects in respect to other ones in information systems. For decision tables we can use credibility coefficients based on decision rules. Knowledge discovery methods can extract rules from an information system. The knowledge represented by the rules may be not exact due to improper data. Calculation of credibility coefficients is based on an assumption that majority of data is correct and only a minor part may be improper. The main purpose of using credibility coefficients is to indicate to which group a particular object probably belongs. A main focus of the paper is set on an algorithm of calculating credibility coefficients and a presentation how credibility coefficients can be used. The algorithm of presented credibility coefficients is based on decision rules, which are generated using the rough set theory. Some remarks on practical results of identifying improper data by credibility coefficients are inserted in the paper as well.

Słowa kluczowe

EN

credibility coefficients; information system; decision tables; decision rules; rough set theory

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2008
• Tom: Vol. 1(10)
• Strony: 61--73
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Podraza R.

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

autor

Walkiewicz M.

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

autor

Dominik A.

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

Bibliografia

• 1. Podraza R., Walkiewicz M., Dominik A., (2006). Credibility Coefficients Based on Decision Rules, Proc. of AAIA Conference, Wisła, Poland.
• 2. Podraza R., Walkiewicz M., Dominik A., (2007). Application of Credibility Coefficients Based on Decision Rules, Proc. IX Intl Conf. on Artificial Intelligence AI-22'2007, Artificial Intelligence Studies, Vol. 2(27)/2007, 119-126.
• 3. Podraza R., Walkiewicz M., Dominik A., (2005). Credibility Coefficients in ARES Rough Set Exploration system, Proc. 10th Intl Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, Regina, Canada, LNAI 3642, Part II, Springer-Verlag, 2005, 29-38.
• 4. Podraza R., Tomaszewski K., (2006). KTDA: Emerging Patterns Based Data Analysis System, Annales Universitatis Mariae Curie-Skłodowska, Informatica, AI, Vol.4, Lublin, Poland, 279-290.
• 5. Podraza R., Dominik A., (2005). Credibility Coefficients for Objects of Rough Sets, Proc. of VII Intl Conf. on Artificial Intelligence AI-20'2005, Artificial Intelligence Studies, Special Issue, Vol. 2, 25, 2005, 205-210.
• 6. Podraza R., Walkiewicz M., Dominik A., (2006). Credibility Coefficients Based on Frequent Sets, to appear in Annales Universitatis Mariae Curie-Skłodowska, Sectio AI Informatica.
• 7. Podraza R., Dominik A., Walkiewicz M., (2005). Application of ARES Rough Set Exploration System for Data Analysis, Annales Universitatis Mariae Curie-Skłodowska, Sectio AI Informatica, vol. III.
• 8. Pawlak Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer, Dordrecht.
• 9. Pawlak Z. (1982). Rough Sets, Intl J. of Computer Information Sciences, 11.
• 10. Skowron A. (1995). Extracting Laws from Decision Tables: A Rough Set Approach, Computational Intelligence, 11.
• 11. Podraza R., Dominik A., Walkiewicz M., (2003). Decision Support System for Medical Applications, Proc. of the IASTED Intl Conf. on Applied Simulations and Modeling, Marbella, Spain, 329-334.
• 12. Podraza R., Podraza W., (2002). Rough Set System with Data Elimination, Proc. of the 2002 Intl Conf. on Mathematics and Eng. Techniques in Medicine and Biological Sciences (METMBS’2002), Las Vegas, Nevada, USA, 493-499.
• 13. UCI Repository of Machine Learning Databases and Domain Theories (http://WWW.ics.uci.edu).