Abstract
The variable precision rough sets (VPRS) model is parametric and there are many types of knowledge reduction. Among the present various algorithms, β is introduced as prior knowledge. In some applications, it is not clear how to set the parameter. For that reason, it is necessary to seek an approach to realize the estimation of β from the decision table, avoiding the influence of β apriority upon the result. By studying relative discernibility in measurement of decision table, it puts forward algorithm of the threshold value of decision table’s relative discernibility: choosing β within the interval of threshold value as a substitute for prior knowledge can get knowledge reduction sets under certain level of error classification, thus finally realizing self-determining knowledge reduction from decision table based on VPRS.
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.
Similar content being viewed by others
References
Wang, G., He, X.: A self-learning model under uncertain condition. Journal of Software 14(6), 1096–1102 (2003)
Wang, G.: Rough set theory and knowledge acquistion. Xi’an Jiaotong University Press, Xi’an (2001)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46(1), 39–59 (1993)
Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston (1991)
Beynon, M.: Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134, 592–605 (2001)
Mi, J., Wu, W., Zhang, W.: Approaches to knowledge reduction based on variable precision rough sets model. Information Sciences 159, 255–272 (2004)
Mi, J., Wu, W., Zhang, W.: Knowledge reducts based on variable precision rough set theory. Systems Engineering Theory and Practice 1, 76–82 (2004)
Kryszkiewicz, M.: Comparative studies of alternative type of knowledge reduction in inconsistent systems. International Journal of Intelligent Systems 16, 105–120 (2001)
Yuan, X., Zhang, W.: The relationships between attribute reduction based on variable precision rough set model and attribute reduction in consistent decision tables. PR & AI 17(2), 196–200 (2004)
Zhang, W., Wu, W., et al.: Theory of method of rough sets. Science press, Beijing (2001)
An, A., Shan, N., Chan, C., Ziarko, N.: Discovering rules for water demand prediction: an enhanced rough set approach. Engineering application and artificial intelligence 9(6), 645–653 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, Y., Zhang, Y., Hu, X. (2006). The Relationships Between Variable Precision Value and Knowledge Reduction Based on Variable Precision Rough Sets Model. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_18
Download citation
DOI: https://doi.org/10.1007/11795131_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)