Title: Data classification mining of university mental health education resources based on global search algorithm

Authors: Jing Fang; Daoxun Wang

Addresses: Marxism College, Henan Polytechnic, Zhengzhou, Henan, China ' Vocational and Technical College, Huanghe Science & Technology University, Zhengzhou, Henan, China

Abstract: To solve the problems of low accuracy and recall rate, as well as long classification mining time in traditional methods, a university mental health education resource data classification mining method based on global search algorithm is proposed. Collect data on university mental health education resources, identify abnormal data using isolated forests and perform correction processing. Extract resource data features using Fisher discriminant criteria and select data features. Build a data classification mining model for university mental health education resources, and use the particle swarm optimisation algorithm in the global search algorithm to construct an optimisation objective function for classification mining. Input the data to be processed into the optimised model to obtain relevant classification mining results. The experimental results show that the proposed method has a mean classification mining accuracy of 98.1%, a mean recall rate of 97.3% and a classification mining time of less than 1.28 s.

Keywords: global search algorithm; university mental health education; resource; classification mining; isolation forests; fisher's discriminant criterion; particle swarm optimisation algorithm.

DOI: 10.1504/IJCAT.2023.138837

International Journal of Computer Applications in Technology, 2023 Vol.73 No.4, pp.278 - 287

Received: 15 Sep 2023
Accepted: 15 Dec 2023
Published online: 31 May 2024 *

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