Instances selection for NN with fuzzy rough technique
Xiaomeng Kang, Xiaopeng Liu, Junhai Zhai, Mengyao Zhai · 2011
The NN algorithm is a simple and well-known supervised learning scheme which classifies an unseen instance by finding its closest neighbor in training set. The main drawback of NN is that the whole training set must be stored in the computer to classify an unseen instance. In order to deal with this problem, P. Hart proposed the condensed nearest neighbor (CNN) algorithm. However, CNN select the important instances from the whole training set, which suffers from the problem of large memory requirement same as NN. In this paper, we propose an algorithm to select instances from the border region with fuzzy rough technique. The experimental results demonstrate the effectiveness of our proposed method.