Title: Machine learning model training method and device based on artificial intelligence

Authors: Danping Chen; Han Zhou; Xiaoli Lin; Yanpei Song

Addresses: College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China ' College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China ' College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China ' College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China; School of Materials Science, Henan University of Science and Technology, Luoyang, 471023, Henan, China

Abstract: As an important research direction in artificial intelligence (AI), machine learning (ML) has been widely used in many complex systems. This paper aimed to study how to improve and train graph based semi-supervised learning algorithm (GBSSLA) based on ML. This paper chooses decision trees (DT) and backpropagation neural networks (BPNN) as classifiers to train ML models. Experimental analysis shows that when the labelled data accounts for 20%, 50%, and 80% of the training set, the average error improvement rate of the improved graph based semi supervised learning algorithm (IGBSSLA) is always higher than that of the self training algorithm (STA) and cooperative training algorithm (CTA). From the experimental results, it could be seen that under the same experimental conditions, the same experimental data and the same classifier method, the final error of IGBSSLA and the percentage of error increase were better than STA and CTA.

Keywords: machine learning model; artificial intelligence; training method; GBSSLA; graph based semi supervised learning algorithm.

DOI: 10.1504/IJDS.2024.142824

International Journal of Data Science, 2024 Vol.9 No.3/4, pp.333 - 352

Received: 18 Jun 2024
Accepted: 04 Sep 2024
Published online: 23 Nov 2024 *

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