Sage Journals: Discover world-class research

Abstract

The choice of attribute significance is the most important step of attribute reduction algorithm. Information entropy is a method of calculating the importance of attributes. Due to the fact that information view only takes the size of knowledge granularity into account rather than measures the importance of attributes objectively and comprehensively this paper begins with putting forward the definition of approximate boundary accuracy based on algebra view. Afterwards, this paper proposes two concepts of relative information entropy and enhanced information entropy based on the definition of relative fuzzy entropy, which has obvious magnification effect. Then, two new methods of attribute reduction are proposed by incorporating approximate boundary precision into relative information entropy and enhanced information entropy, so that the choice of the importance of the attribute is more objective and comprehensive. Finally, it will analyze and compare the classification accuracy of each kind of algorithm by using the SVM classifier and ten-fold crossover method, and analyze the influence of outliers on the effect of the algorithm. Through experimental analysis and comparison, it can be concluded that the attribute reduction based on improved entropy is feasible and effective.

Keywords

Attribute reduction approximate boundary accuracy relative information entropy enhanced information entropy blend entropy

Get full access to this article

View all access options for this article.

References

Pawlak and

Skowron , Rough sets: Some extensions, Inform Sci 117(1) (2007), 28–40.

Pawlak , Rough sets, Int J Comput Inform Sci 11(5) (1982), 341–356.

Zhang and

Wei , Rules acquisition and attribute reduction of ordered formal decision contexts, PR AI 29(11) (2016), 976–984.

C.C.

Huang ,

Tzu-Liang , Tseng ,

F.H.

Jiang , et al., Rough set theory: A novel approach for extraction of robust decision rules based on incremental attributes, Ann Oper Res 216(1) (2014), 163–189.

Kim ,

Y.Y.

Chu ,

J.Z.

Watada , et al., A DNA-based algorithm for minimizing decision rules: A rough sets approach, IEEE Trans Nanobiosci 10(3) (2011), 139–151.

Tao ,

Jialin ,

Xudong and

Houhe , Research of aircraft generator fault diagnostic decision based on attribute reduction in variable precision rough sets, Appl Res Comput 34(4) (2017), 1101–1104.

Ying and

Gaopen , Goal mine safety risk prediction by RS-SVM combined model, J China Univ Mining Technol 46(2) (2017), 423–429.

Baohua and

Shiyi , Fast attribute reduction algorithm based on row storage, Pattern Recogn ArtifIntell 8(9) (2015), 795–801.

Bowen ,

Guohe ,

Weijiang , et al., Attribute reduction method based on enhanced positive region, Appl Res Comput 34(1) (2017), 107–109.

10.

Taotao ,

Fumin and

Tengfei , Incremental algorithm for attribute reduction based on positive region and dis-cernibility element, Comput. Eng 42(8) (2016), 183–187, 193.

11.

Hao and

Chao , Incremental updating algorithm for attribute reduction based on improved discernibility matrix, Comput Sci 42(6) (2015), 251–255.

12.

Yu , Attribute reduction with rough set based on dis-cernibility information tree, Control Dec 30(8) (2015), 1531–1536.

13.

C.E.

Shannon , The mathematical theory of communication, Bell Syst Tech J 27(3-4) (1948), 373-423.

14.

Guoying ,

Hong and

Dachun , Decision table reduction based on conditional information entropy, Chin J Comput 25(7) (2002), 759–776.

15.

Ming , Approximate reduction based on conditional information entropy in decision tables, Acta Electron Sin 35(11) (2007), 2156–2160.

16.

Qinghua and

Yu , New attribute reduction on information entropy, J Front Comput Sci Technol 7(4) (2013), 359–367.

17.

Feng ,

Shasha ,

Junwei , et.al., Attribute reduction based on approximation decision entropy, Control Decision 30(1) (2015), 65–70.

18.

J.R.

Vergara and

P.A.

Estevez , A review of feature selection methods based on mutual information, Neural Comput Appl 24(1) (2014), 175–186.

19.

Yuhua ,

Jiye ,

Pedrycz , et al., Positive approximation: An accelerator for attribute reduction in rough set theory, ArtifIntell 174(9) (2010), 597–618.

20.

Yu ,

Yintian and

Chao , Fast algorithm for attribute reduction based on bucket sort, Control Decision 26(2) (2011), 207–212.

Attribute reduction based on improved information entropy

Abstract

Keywords

Get full access to this article

References