A network big data classification method based on decision tree algorithm Online publication date: Tue, 19-Mar-2024
by Nian Xiao; Siguang Dai
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 1, 2024
Abstract: Aiming at the problem of low accuracy and low efficiency of network big data classification, a new network big data classification method based on decision tree algorithm is designed. First, the crawler manager circularly collects network big data, sets the collection threshold and randomly generates crawler signatures, so as to continuously collect and update data. Then, the directed graph of network big data is constructed that automatically select and extract the key feature attributes of network big data, and the interference factors of feature data are extracted. Finally, the network big data classification decision tree is constructed to obtain the optimal gain data, the node attributes of the data are determined, and the classification algorithm design combined with recursive call rules and classification termination conditions is completed. Experimental results show that the algorithm can improve the accuracy and efficiency of data classification.
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