Title: A hybrid ABC-SVM approach for multi-dimensional data classification with synthetic data balancing

Authors: Weili Zhao; Yuan Xu; Chuzhen Wang

Addresses: College of Marxism, Dongguan City University, Dongguan, 523419, China ' College of Artificial Intelligence, Dongguan City University, Dongguan, 523419, China; Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China ' College of Artificial Intelligence, Dongguan City University, Dongguan, 523419, China

Abstract: User behaviour data plays a vital role in digital decision-making, especially in education, finance, and healthcare. However, traditional methods often fail to capture the complex characteristics of user behaviour, perform poorly on multi-dimensional data, and struggle with class imbalance, which limits model performance. To overcome these challenges, this study constructs a dynamic user behaviour dataset from the Chaoxing system and adopts the synthetic minority oversampling technique (SMOTE) to address data imbalance problem. The artificial bee colony (ABC) algorithm is combined with the support vector machine (SVM) to optimise model parameters and improve performance. Experimental results show that the proposed ABC-SVM model performs well in complex classification tasks with an accuracy of 97.9%, outperforming baseline and other optimisation methods. This study highlights the potential of intelligent optimisation algorithms in multi-dimensional data analysis and provides a reference for intelligent systems in other fields.

Keywords: class imbalance; multi-dimensional data; support vector machine; algorithm optimisation; ideological and political education; educational assessment.

DOI: 10.1504/IJES.2025.144931

International Journal of Embedded Systems, 2025 Vol.18 No.1, pp.29 - 38

Received: 28 Sep 2024
Accepted: 09 Dec 2024
Published online: 11 Mar 2025 *

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