Sorting decision model for dynamic fault tolerance based on dominance relation rough set

被引：0

作者：

机构：

[1] School of Information Science and Technology, Southwest Jiaotong University

[2] Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences

[3] School of Computer Science and Engineering, Chongqing University of Technology

[4] Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications

来源：

Wang, G. (wanggy@cqupt.edu.cn) | 1600年 / Science Press卷 / 49期

关键词：

Decision making; Dominance relation; Fault tolerance; Rough set; Sorting;

D O I：

10.3969/j.issn.0258-2724.2014.01.023

中图分类号：

学科分类号：

摘要：

To enhance the fault-tolerant capacity of the dominance relation rough set model in solving sorting decision problems, three efficient sorting decision algorithms are proposed by regarding the fault-tolerant processing as a dynamic adjusting process according to the fault tolerance direction of the user's preference, i.e., upward, downward, or synthesis of the both. The boundary objects are initially ranked by the proposed algorithms, and the obtained results are adjusted using the coverage information as the heuristic criteria to achieve a accurate or near accurate sorting of the object finally. In contrast to the variable-consistency dominance-based rough set approach (VC-DRSA), the proposed algorithms do not need prior domain knowledge to determine and adjust a threshold. Application of the algorithms to wine quality dataset show that the proposed methods can achieve a 21.34% improvement in average sorting accuracy and a 50.91% reduction in average mis-sorting cost, compared with the existing methods.

引用

页码：147 / 152

页数：5

共 19 条

[1] Zopounidis C., Doumpos M., Multicriteria classification and sorting methods: A literature review, European Journal of Operational Research, 138, 2, pp. 229-246, (2002)
[2] An L., Chen Z., Sorting decision model based on binary relations and Boolean reasoning, Journal of System Engineering, 24, 6, pp. 701-709, (2009)
[3] Chen S., Liu J., Wang H., Et al., Ordering based decision making - A survey, Information Fusion, 14, 4, pp. 521-531, (2013)
[4] Hu Q., Guo M., Yu D., Et al., Information entropy for ordinal classification, Science China Information Sciences, 53, 6, pp. 1188-1200, (2010)
[5] Costa J.F.P., Alonso H., Cardoso J.S., The unimodal model for the classification of ordinal data, Neural Networks, 21, 1, pp. 78-91, (2008)
[6] Peng Y., Kou G., Wang G., Et al., FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms, Omega, 39, 6, pp. 677-689, (2011)
[7] Dembczynski K., Kotlowski W., Slowinski R., Ordinal Classification with Decision Rules, Mining Complex Data, pp. 169-181, (2008)
[8] Xia F., Zhang W., Li F., Et al., Ranking with decision tree, Knowledge and Information Systems, 17, 3, pp. 381-395, (2008)
[9] Greco S., Matarazzo B., Slowinski R., Rough sets theory for multicriteria decision analysis, European Journal of Operational Research, 129, 1, pp. 1-47, (2001)
[10] Greco S., Matarazzo B., Slowinski R., Rough approximation by dominance relations, International Journal of Intelligent Systems, 17, 2, pp. 153-171, (2002)

← 1 2 →