Evaluation method of e-government audit information based on big data analysis

IF 3.6
Jingui He , Hansi Ya
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引用次数: 0

Abstract

With the continuous growth of e-government data, traditional audit methods face increasing limitations in handling large-scale data, leading to low processing efficiency and insufficient accuracy. To address these challenges, this paper proposes a big data-driven evaluation and prediction model for e-government audit information. The proposed method is built on a Hadoop-based distributed computing platform, which supports heterogeneous data integration and efficient parallel processing. Furthermore, a parallel PSO-RF algorithm combining Particle Swarm Optimization (PSO) and Random Forest (RF) is designed to enhance classification performance and computational efficiency. Experiments are conducted using e-government audit data from a Chinese province collected between 2018 and 2020, covering 15 audit categories. The model performance is comprehensively evaluated using accuracy, recall, F1-score, and AUC metrics. Results demonstrate that the proposed parallel PSO-RF algorithm outperforms conventional RF and Support Vector Machine (SVM) approaches across multiple indicators, with a maximum prediction deviation of only 0.28 % compared to actual audit issue probabilities. This study not only improves the accuracy and efficiency of audit information processing but also provides a scalable technical approach and theoretical foundation for intelligent audit evaluation and risk assessment in e-government systems.
基于大数据分析的电子政务审计信息评价方法
随着电子政务数据的不断增长,传统的审计方法在处理大规模数据时面临越来越大的局限性,导致处理效率低,准确性不足。针对这些挑战,本文提出了一个大数据驱动的电子政务审计信息评价与预测模型。该方法建立在基于hadoop的分布式计算平台上,支持异构数据集成和高效并行处理。在此基础上,结合粒子群算法(PSO)和随机森林算法(RF)设计了一种并行PSO-RF算法,提高了分类性能和计算效率。实验使用了2018年至2020年间收集的中国某省的电子政务审计数据,涵盖了15个审计类别。模型性能使用准确性、召回率、f1分数和AUC指标进行综合评估。结果表明,所提出的并行PSO-RF算法在多个指标上优于传统的RF和支持向量机(SVM)方法,与实际审计问题概率相比,最大预测偏差仅为0.28%。本研究不仅提高了审计信息处理的准确性和效率,而且为电子政务系统智能审计评价和风险评估提供了可扩展的技术途径和理论基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.20
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