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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...
In this paper, we propose a fast semi-supervised learning algorithm based on the bisecting clustering. The key idea of the proposed algorithm is dividing data into two sub clusters each time by using bisecting clustering and parts of the features of the data. The time complexity of the algorithm is nearly linear to the data size. Numerical comparisons with several existing methods for the UCI datasets...
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