Abstract
The Variable Precision Rough Sets Model (VPRS) is an extension of the original Rough Set Theory. To employ VPRS analysis the decision maker (DM) needs to define satisfactory levels of quality of classification and β (confidence) value. This paper considers VPRS analysis when the DM only defines a satisfactory level of quality of classification. Two criteria for selecting a β-reduct under this condition are discussed. They include the use of permissible β intervals associated with each β-reduct. An example study is given illustrating these criteria. The study is based on US state level data concerning motor vehicle traffic fatalities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
An, A., Shan, N., Chan, C., Cercone, N., Ziarko, W.: Discovering Rules for Water Demand Prediction: An Enhanced Rough-Set Approach. Engng. Applic. Artif. Intell. 9 (1996) 45–653
Fayyad, U.M., Irani, K.B.: On the Handling of Continuous-Valued Attributes in Decision Tree Generation. Machine Learning 8 (1992) 87–102
Katzberg, J.D., Ziarko, W.: Variable Precision Rough Sets with Asymmetric Bounds. In: Ziarko, W. (ed.): Rough Sets, Fuzzy Sets and Knowledge Discovery (1993) 167–177
Katzberg, J.D., Ziarko, W.: Variable precision extension of rough sets. Fundamenta Informaticae 27 (1996) 155–168
Kryszkiewicz, M.: Maintenance of Reducts in the Variable Rough Set Model. In: Lin, T.Y. and Cercone, N. (eds.): Rough Sets and Data Mining: Analysis of Imprecise Data, Kluwer Academic Publishers, London, (1997) 355–372
Pawlak, Z.: Rough Sets. International Journal of Information and Computer Sciences 11 (1982) 341–356
Pawlak, Z.: Rough Sets-Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers London 1991
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Slowinski, R. (ed.): Intelligent Decision Support: Handbook of Applications and Advances of Rough Sets Theory, Kluwer Academic Publishers (1992) 331–362
Slowinski, R., Zopounidis, C., Dimitras, A.I.: Prediction of company acquisition in Greece by means of the rough set approach. European Journal of Operational Research 100 (1997) 1–15
Washington, S., Metarko, J., Fomumung, I., Ross, R., Julian, F., Moran, E.: An interregional comparison: fatal crashes in the Southeastern and non-Southeastern United States: preliminary findings. Accident Analysis and Prevention 31 (1999) 135–146
Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46 (1993) 39–59
Ziarko, W.: Analysis of Uncertain Information in the Framework of Variable Precision Rough Sets. Foundations of Computing and Decision Sciences 18 (1993) 381–396
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Beynon, M. (2001). An Investigation of β-Reduct Selection within the Variable Precision Rough Sets Model. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_13
Download citation
DOI: https://doi.org/10.1007/3-540-45554-X_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43074-2
Online ISBN: 978-3-540-45554-7
eBook Packages: Springer Book Archive