Evidence Theory of One-Dimensional Compression KNN Classification Method

Abstract:

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As only using Euclidean distance KNN algorithm has its limits, many researchers use other distance calculation methods as the replacement it to improve the accuracy of Data Classification. While combining the DS evidence theory with a series of KNN algorithm which discussed in this paper, we found that every algorithm has their merits. All of them ignore the analysis of the data set, through deeply analysis we found that the actual distance is determined by the larger value when two attribute values are in great difference. Therefore, what we do next is to compress the large-dimensional numerical data values. By this way, the accuracy of KNN, VSMKNN, KERKNN algorithm are obviously improved after experiment and then these new methods are called CDSKNN, CDSVSMKNN, CDSKERKNN.

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Edited by:

H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong

Pages:

1337-1341

DOI:

10.4028/www.scientific.net/AMR.143-144.1337

Citation:

W. F. Yan et al., "Evidence Theory of One-Dimensional Compression KNN Classification Method", Advanced Materials Research, Vols. 143-144, pp. 1337-1341, 2011

Online since:

October 2010

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Price:

$35.00

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