DocumentCode :
2680274
Title :
KNN algorithm improving based on cloud model
Author :
Yu, Liu ; Gui-Sheng, Chen
Author_Institution :
State Key Lab. of Software Dev. Environ., Beihang Univ., Beijing, China
Volume :
2
fYear :
2010
fDate :
27-29 March 2010
Firstpage :
63
Lastpage :
66
Abstract :
KNN algorithm is particularly sensitive to outliers and noise contained in the training data set. In this paper, we use the reverse cloud algorithm to map the training samples into clouds. Each attribute is mapped to a cloud vector. Reverse cloud algorithm is not sensitive to the noise on data sets and it can eliminate the impact of noise on classification effectively. By comparing the similarity of clouds in the cloud vector, we can calculate the attributes weights. For those attributes with a low weight of properties, we find out merger them to a new attribute which can generate more significant attribute weight than original ones. We present a new KNN algorithm based on Cloud Model and compare our algorithm with classic KNN algorithms and other well-known improved KNN algorithms using 10 data sets. Experiments show that our approach could achieve a better or at least a comparable classification accuracy with other algorithms.
Keywords :
learning (artificial intelligence); pattern classification; KNN algorithm; attribute weight learning; cloud model; cloud vector mapping; data set training; reverse cloud algorithm; Clouds; Corporate acquisitions; Data engineering; Nearest neighbor searches; Programming; Software algorithms; Training data; Uncertainty; Voting; Working environment noise; Cloud Model; KNN; attribute weight learning; classification; similarity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
Type :
conf
DOI :
10.1109/ICACC.2010.5487185
Filename :
5487185
Link To Document :
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