Abstract
On fundamental aspect of variable precision rough approximate reduction is an important mechanism for knowledge discovery. This paper mainly deals with attribute reductions of an inconsistent decision information system based on a dependence space. Through the concept of inclusion degree, a generalized decision distribution function is first constructed. A decision distribution relation is then defined. On the basis of this decision distribution relation, a dependence analogy relation representation of VPRS data space is proposed, and an equivalence congruence based on the attribute sets is also obtained. Applying the congruence on a dependence space, new approaches to find a distribution consistent set are formulated. The theorems for judging distribution consistent sets are also established by using these congruences and the decision distribution relation.
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References
Pawlak, Z.: Rough Sets. Intemational Journal of Information and Computer Seienee 11, 13–34 (2008)
Kattan, M., Copper, R.: The Predietive accuracy of computer-based classification decision techniques. A Review and Research Directionso, MEGA 26(4), 452–467 (1998)
Zattan, W.: Variable preision rought set model. Journal of Computer and Systerm Science 46(1), 39–59 (1993)
Katzberg, J.D., Ziarko, W.: Vatriable Preeision rough sets with asymmetric bounds, in Banff, Alberta, Can (2001)
Tseng, T., Kwon, Y.J., Erterkin, Y.M.: Feature-baseed ruld inductin inmachining opration using rough set theory for quality assurance. Robotics and Computer-Intergrated Manufasctuing 21, 559–567 (2008)
Li, R.P., Wang, Z.O.: Mining classification rules using set and neural networks. European Journal of Operational Reaseach 157, 439–449 (2004)
Gong, Z.T., Sun, B.Z., Shao, Y.B., et al.: Variable Preeision rough set model based on general relation. In: Proeeedings of 2004 Intemational Conference on Machine Learning and Cybemetics, Shanghai, China, pp. 2490–2494 (2004)
Tsumoto, S., Ziarko, W., Shan, N.: Kowledge diseovery in clinieal databases based on variable precision rough set model. In: Proc. Annu. Symp. Comput. APPI Med. Care, p. 270 (2007)
Mieszkowicz-Rolka, A., Rolka, L.: Variable Precision Fuzzy Rough Sets Model in the Analysis of Process Data. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 354–363. Springer, Heidelberg (2005)
Nishino, T., Nagamaehi, M., Tanaka, H.: Variable Precision Bayesian rough set model and its applieation to human evaluation data. In: Proeeeding so FSPIE-The Intemational Soeiety for Optical Engineering, San Jose, United States, pp. 294–303 (2010)
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Liu, B., Guo, H. (2011). Theory and Algorithm Based on the General Similar Relationship between the Approximate Reduction. In: Liu, B., Chai, C. (eds) Information Computing and Applications. ICICA 2011. Lecture Notes in Computer Science, vol 7030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25255-6_17
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DOI: https://doi.org/10.1007/978-3-642-25255-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25254-9
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