A weighted uncertainty measure of rough sets based on general binary relation

被引：0

作者：

Teng, Shu-Hua ^{[1
]}

Lu, Min ^{[1
]}

Yang, A-Feng ^{[1
]}

Zhang, Jun ^{[1
]}

Zhuang, Zhao-Wen ^{[1
]}

机构：

[1] College of Electronic Science and Engineering, National University of Defense Technology

来源：

Jisuanji Xuebao/Chinese Journal of Computers | 2014年 / 37卷 / 03期

关键词：

Entropy; General binary relation; Rough sets; Uncertainty; Weighted measure;

D O I：

10.3724/SP.J.1016.2014.00649

中图分类号：

学科分类号：

摘要：

Uncertainty measure is one of the important aspects of rough set theory. A new kind of weighted uncertainty measure called α-entropy is presented under general binary relation by considering the sample data with different importance, and some existing uncertainty measures are a special case of α-entropy by adjusting the variable parameter α. Thus it unites the corresponding uncertainty measures of complete and incomplete information systems. In addition, a well-justified uncertainty measures, α-roughness (α-accuracy) is proposed based on α-entropy. It is proved that α-roughness (α-accuracy) decreases (increases) monotonously as the information granularities become smaller. The numerical example proves that the α-accuracy and α-roughness are more reasonable and accurate than the existing methods. Finally, under general binary relation, a new heuristic weighted attribute reduction algorithm is proposed based on α-accuracy. The experiments demonstrate that the weighted measures in this paper provide a method for combining the subjective preferences and prior knowledge in uncertainty measures, and the combination classifier based on the variable parameter α can improve the accuracy of classification. These investigations developed the uncertainty theory and provide theory basis for knowledge acquisition in information systems based on general binary relation.

引用

页码：649 / 665

页数：16

共 50 条

[31] Zhang S., Chen Q., Yang Q., Acquiring knowledge from inconsistent data sources through weighting, Data & Know-ledge Engineering, 69, 8, pp. 279-299, (2010)
[32] Own H.S., Abraham A., A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice, Applied Soft Computing, 12, 3, pp. 999-1005, (2012)
[33] Greco S., Matarazzo B., Slowinski R., Rough approximation of a preference relation by dominance relations, European Journal of Operational Research, 117, 1, pp. 63-83, (1999)
[34] Slowinski R., Vanderpooten D., A generalized definition of rough approximations based on similarity, IEEE Transactions on Knowledge and Data Engineering, 12, 2, pp. 331-336, (2000)
[35] Wang C.Z., Wu C.X., Chen D.G., A systematic study on attribute reduction with rough sets based on general binary relations, Information Sciences, 178, 9, pp. 2237-2261, (2008)
[36] Miao D.-Q., Wang G.-Y., Liu Q., Et al., Granular Computing: Last, Present and Future, (2007)
[37] Hardy G., Littlewood J.E., Polya G., Inequalities, (1952)
[38] Wang J.H., Liang J.Y., Qian Y.H., Et al., Uncertainty measure of rough sets based on a knowledge granulation of incomplete information systems, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16, 2, pp. 233-244, (2008)
[39] Zhang W.-X., Wu W.-Z., Liang J.-Y., Et al., Theory and Approach of Rough Set, (2001)
[40] Yang Y.J., John R., Global roughness of approximation and boundary rough sets, Proceedings of the Computational Intelligence, pp. 1106-1111, (2008)

← 1 2 3 4 5 →