Abstract
Feature selection algorithm based on method-difference-similitude matrix (DSM) is a better method of data mining. In this method, for storing D-matrix and S-matrix, the efficiency of the algorithm is seriously affected when the massive data sets are considered. So we use the idea of the old algorithm to design a new feature selection algorithm which need not store D-matrix and S-matrix. The complexity of the new algorithm are better than that of the old. At last, an example is used to illustrate the efficiency of the new algorithm.
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Wu, W., Xu, Z., Liu, J. (2009). Efficient Feature Selection Algorithm Based on Difference and Similitude Matrix. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_16
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DOI: https://doi.org/10.1007/978-3-642-01216-7_16
Publisher Name: Springer, Berlin, Heidelberg
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