Nearest Neighbor Classification by Partially Fuzzy Clustering
Lifei Chen, Gongde Guo, Shengrui Wang · 2012
The k-Nearest-Neighbours(kNN) is a simple and effective method for data classification. One of the major drawbacks of kNN is its low efficiency in its testing phase due to the lack of an explicit classification model. Recently, kNN model-based classifiers have been proposed to improve the conventional kNN. However, their building incurs high computational costs. In this paper, we tackle the model building problem by developing a cluster-based training algorithm to learn an optimized set of representatives that approximate the distributions of training data. The training algorithm adopts a fuzzy clustering method for unsupervised learning on the partial training set, and has a linear time complexity with respect to the size of the set. The experimental results conducted on real-world datasets demonstrate that the new method outperforms the previous kNN model-based classifier in the accuracy and possesses outstanding efficiency compared to kNN based classifiers.