DocumentCode :
467810
Title :
Fast Nearest Neighbor Classification using Class-Based Clustering
Author :
Chen, Tung-Shou ; Chiu, Yung-Hsing ; Lin, Chih-Chiang
Author_Institution :
Nat. Taichung Inst. of Technol., Taichung
Volume :
4
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
1894
Lastpage :
1898
Abstract :
Nearest neighbor rule (NNR) is a parameter-free classifier which is easy to implement, simple to operate and with high accuracy. However, it is time and memory consuming for large datasets. This study proposed a parameter-free method to accelerate NNR. This method employs a class-based clustering algorithm to divide the training data to several clusters with respective members belonging to the same class. Cluster representations are extracted clustering border data based on the nearest neighbors between the different class clusters. Since the cluster representations are the clustering border data rather than the clustering centers, the predicting accuracy will not be affected by removing a cluster´s internal data. In the predicting phase, the nearest neighbor search area is narrowed down by referring to a distance between a testing data and its nearest cluster. Thus the predicting process is speeded up. In this paper, the performance of the proposed method was evaluated and compared with NNR, K-NNR, and LIBSVM by using 5 benchmark datasets. Experimental results show that the proposed parameter-free classification algorithm is very easy to operate and gives consideration to speed and accuracy.
Keywords :
pattern classification; pattern clustering; class-based clustering; cluster representations; fast nearest neighbor classification; nearest neighbor rule; parameter-free classifier; Acceleration; Accuracy; Classification algorithms; Clustering algorithms; Computer science; Cybernetics; Data mining; Machine learning; Nearest neighbor searches; Training data; Accelerating; Classification; Clustering; Nearest neighbor; Parameter-Free;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370457
Filename :
4370457
Link To Document :
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