A method of removing redundant information from multidimensional data based on Bayesian algorithm Online publication date: Fri, 25-Oct-2024
by Chunlei Ren; Yanhua Wu; Demin Ma
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 4, 2024
Abstract: In order to solve the problems of poor anti-interference, high false detection rate of redundant information features, and high time cost of redundant removal in traditional methods, this paper proposes a method of removing redundant information from multidimensional data based on Bayesian algorithm. Firstly, noise information and interference information in multidimensional data are filtered. Secondly, the preprocessed data is segmented to extract the features of redundant information. Then, the fitness function of redundant information is used to classify redundant information features. Finally, the association model between nodes is established in the Bayesian network, and the redundant information is filtered using the Hash function. The experimental results show that the design method has good anti-jamming performance, the global maximum false detection rate is 5.29%, and the maximum value of the time cost to remove redundancy is 213.06 s, which shows that the method has good application performance.
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