An improved heuristic attribute reduction algorithm based on information entropy in rough set

被引：0

作者：

Yang, Su-Min ^{[1
,2
]}

Meng, Jie ^{[1
]}

Zhang, Zheng-Bao ^{[2
]}

Xie, Zhi-Ying ^{[2
]}

机构：

[1] State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, 471003, Henan

[2] Department Of Information Engineering, Shijiazhuang Mechanical Engineering College, Shijiazhuang

来源：

Open Cybernetics and Systemics Journal | 2015年 / 9卷 / 01期

关键词：

Attribute reduction; Mutual information; Non-core information system; Rough set;

D O I：

10.2174/1874110X01509012774

中图分类号：

学科分类号：

摘要：

At present, in rough set theory there are two kinds of heuristic attribute reduction algorithms, one is based on discernibility matrix, the other is based on mutual information. But if these algorithms are applied to the non-core information system, there will be much problems, such as too much calculation, excessive reduction, or insufficient reduction. So we propose an improved heuristic attribute reduction algorithm on the basis of rough set theory, in which the attribute importance is dependent on two factors, one is increment of mutual information, the other is information entropy. And we set the attribute with both the largest attribute importance and mutual information among all attributes as the core attribute, by which we solve the problem that causes the computational complexity increasing because of selecting the initial attribute randomly. By the proposed algorithm we can not only improve the efficiency of attribute reduction, but decrease the number of attribute reduction. The validity of the proposed algorithm is verified by two ways of the theoretic analysis and the simulation experiments. © Su-Min et al.

引用

页码：2774 / 2779

页数：5

共 22 条

[1]

Wong S.K.M., Ziarko W., On optional decision rules in decision tables, Bulletion of Polish Academy of Sciences, 33, pp. 693-696, (1985)

[2]

Skowron A., Rauszer C., The discernibility matrices and functions in information systems, Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, pp. 331-362, (1992)

[3]

Hu X.H., Cercone N., Learning in relational databases: A rough set approach, Computational Intelligence: An International Journal, 11, pp. 323-338, (1995)

[4]

Yang M., Sun Z., Improvement of Discernibility Matrix r the Computation of a core, Journal of Fudan University (Natural Science), 43, pp. 865-868, (2004)

[5]

Zhao Z., An Improvement Method of A Attribute Reduction, Science & Technology, 32, pp. 179-181, (2006)

[6]

Cheng J., Zhu J., Zhang F., An updated algorithm for attribute reduction based on discernibility matrix, Journal of Hunan University (Natural Sciences), 36, pp. 86-88, (2009)

[7]

Jiang Y., Wang X., Ye Z., Attribute reduction algorithm of rough sets based on discernibility Matrix, Journal of System Simulation, 20, pp. 3718-3721, (2008)

[8]

Chen Z., Zhang Q.H., Study on Attribute Reduction Algorithm Based on Fuzzy Rough Set, (2006)

[9]

Yang M., Yang P., Approximate reduction based on conditional information entropy over vertically partitioned multidecision table, Control and Decision, 23, pp. 1103-1107, (2008)

[10]

Zhang W., Zhangyan W.X., An attribute reduction algorithm combining probability heuristic, Information and Knowledge Granularity, Computer Applications and Software, 30, pp. 43-46, (2013)

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