[1] Sun Liang, Han Chongzhao,. Knowledge discovery methodfor feature-decision level fusion of multiple classifiers [J]. Journal of Southeast University (English Edition), 2006, 22 (2): 222-227. [doi:10.3969/j.issn.1003-7985.2006.02.017]

Copy

Knowledge discovery methodfor feature-decision level fusion of multiple classifiers()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 2

Page:

222-227

Research Field:

Computer Science and Engineering

Publishing date:

2006-06-30

Info

Title:

Knowledge discovery methodfor feature-decision level fusion of multiple classifiers

Author(s):

Sun Liang¹;  2;  Han Chongzhao¹

¹ School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
² Department of Electrical Information Engineering, Institute of Information Science and Technology, Zhengzhou, 450001, China

Keywords:

multiple classifier fusion;  knowledge discovery;  Dempster-Shafer theory;  generalized rough set;  hyperspectral

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2006.02.017

Abstract:

To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery.In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability.The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables(DTs)in the form of mass function in the Dempster-Shafer theory(DST).Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST.The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting(PV).

References:

[1] Kittler J, Hatef M, Duin RPM, et al.On combining classifiers [J].IEEE Trans Pattern Analysis and Machine Intelligence, 1998, 20(3):226-239.
[2] Parhami B.Voting:a paradigm for adjudication and data fusion in dependable systems [A].In:Diab H B, Zomaya A Y, eds.Dependable Computing System [C].Wiley & Sons, Inc, 2005.
[3] Skowron A, Graymala-Busse J.From rough set to evidence theory [A]. In: Yager R R, Kacprzyk J, Fedrizzi M, eds.Advances in the Dempster-Shafer Theory of Evidence [C].New York:Wiley, 1994. 193-236.
[4] Pawlak Z.Rough set:theoretical aspects of reasoning about data [M].Boston:Kluwer Academic Publishers, 1991.
[5] Yao Y Y.Generalized rough set models [A].In:Polkowski L, Skowron A, eds.Rough Sets in Knowledge Discovery [C].Heidelberg:Physica-Verlag, 1998.286-318.
[6] Slowinski R, Vanderpooten D.A generalized definition of rough approximations based on similarity [J].IEEE Trans Knowledge and Data Engineering, 2000, 12(2):331-336.
[7] Wu W Z, Leung Y, Zhang W X.Connections between rough set theory and Dempster-Shafer theory of evidence [J].International Journal of General System, 2002, 31(4):405-430.
[8] Richards J A, Jia X P.Remote sensing digital image analysis:an introduction[M].3rd ed.Berlin, Springer-Verlag, 1999.322;331.
[9] Ma Y, Chandler J S, Wilkins D C.On the decision making problem in Dempster-Shafer theory[A].In:Proc of the International Symposium on Artificial Intelligence [C].Cancun, Mexico, 1992.7-11.
[10] William D P.The derivative ratio algorithm:avoiding atmospheric effects in remote sensing [J].IEEE Trans Geoscience and Remote Sensing, 1991, 29(3):350-357.

Memo

Memo:

Biographies: Sun Liang(1961—), male, graduate, associate professor, sun_-liang@people.com.cn;Han Chongzhao(1943—), male, professor, czhan@mail.xjtu.edu.cn.

Common functions