Title: A network big data classification method based on decision tree algorithm

Authors: Nian Xiao; Siguang Dai

Addresses: Department of Information Engineering, Wuhan Vocational College of Communications and Publishing, Wuhan 430223, China ' School of Management, Hubei University of Education, Wuhan 430205, China

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.

Keywords: decision tree algorithm; network big data; classification; crawler signature; digraph; information gain; recursive call.

DOI: 10.1504/IJRIS.2024.137442

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.1, pp.66 - 73

Received: 15 Aug 2022
Accepted: 07 Nov 2022
Published online: 19 Mar 2024 *