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Feature selection and weighting are normally ways to improve KNN classification algorithm. 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...
With the increase of the training set??s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result. We adopt a non-parametric k-NN rule called relative neighborhood graph...
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