KNN Algorithm Based on Weighted Entropy of Attribute Value

Xing Jiang Xiao; Wei Qing Wang; Hua Feng Ding; Lu Cao

doi:10.4028/www.scientific.net/AMR.179-180.1000

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 179-180KNN Algorithm Based on Weighted Entropy of...

KNN Algorithm Based on Weighted Entropy of Attribute Value

Abstract:

The traditional KNN algorithm usually adopts European distance formula to measure the distance between two samples. Since each attribute functions differently in the actual sample data collection, the accuracy of the classification will be reduced consequently, this article proposes one method to measure the attribute value and entropy weight, namely KNN algorithm based on weighted entropy of attribute value. The experiment indicated that, compared with the traditional KNN algorithm, the algorithm proposed in this article can not only guarantee the efficiency of classification but also enhance the accuracy of classification.

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Periodical:

Advanced Materials Research (Volumes 179-180)

Pages:

1000-1004

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.179-180.1000

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Online since:

January 2011

Authors:

Xing Jiang Xiao, Wei Qing Wang, Hua Feng Ding, Lu Cao

Keywords:

Cross Validation, K-Nearest Neighbor (KNN), Weighted Entropy

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References

[1] Jiawei Han, Kamber M. Data Mining Concepts and Techniques [M]. Beijing: Machinery Industry Press, 2001.

Google Scholar

[2] C D' Amato , D Malerba , F Esposito , et al . Extending the k-nearest neighbour classification algorithm to symbolic objects [A]. Atti del Convegno Intermedio della Societ à Italiana diStatistica"Analisi Statistica Multivariata per le scienze eco2nomico2sociali , le scienze naturali e la tecnologia" [ C ] . Italia : Napoli , (2003).

Google Scholar

[3] Chen ZhenZhou, Li Lei, Yao Zhengan. KNN algorithm based on SVM weighting [J]. Sun Yat-sen University Natural Science Edition, 2005, 44 (1) : 17 - 20.

Google Scholar

[4] Sang Yingbing, Liu Qingsun. KNN algorithm based on feature weighted [J]. Hanna University of Science and Technology, 2008, Vol. 26 No. 4, 352-355.

Google Scholar

[5] Dou Zengfa. KNN algorithm based on information improvement [J]. Electronics Technology, 2006, 12: 52 - 56.

Google Scholar

[6] Wang Zengmin. KNN algorithm based on entropy improvement [J]. Computer Engineering and Applications, 2009 , 45(30): 130.

Google Scholar