Author(s)

Jiaoli Zhou ¹

Affiliation(s)

¹ Hainan College of Science and Technology, Haikou, 571126, China

Corresponding Author

Jiaoli Zhou

ABSTRACT

This paper explores the impact of high-dimensional feature selection strategies based on machine learning in malicious document detection. Addressing the existing issues in the field of malicious document detection, a new strategy for high-dimensional feature selection utilizing machine learning techniques is proposed. Through empirical research on multiple datasets, the effectiveness of this strategy in enhancing the performance of malicious document detection is evaluated. The results show that high-dimensional feature selection can maintain high accuracy while reducing model complexity and improving detection efficiency.

KEYWORDS

Machine Learning, High-Dimensional Feature Selection, Malicious Document Detection, Performance Evaluation, Model Complexity

CITE THIS PAPER

Jiaoli Zhou, Impact of High-Dimensional Feature Selection Strategies Based on Machine Learning on Malicious Document Detection. Automation and Machine Learning (2024) Vol. 5: 32-38. DOI: http://dx.doi.org/10.23977/autml.2024.050105.

REFERENCES

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[3] Zhang Rong. Machine Learning-based Detection of ACM Performance Degradation Failures [J]. Aviation Maintenance and Engineering. 2023 (11): 40-42.
[4] Yue Xin. Application of Hyperspectral Imaging and Short Video Imaging Combined with Machine Learning in Detecting the Consistency of Fireproof Coatings [J]. Coatings Industry. 2023 (12): 8.
[5] Qin Chuandong. Multi-Strategy Hybrid Artificial Bee Colony Algorithm for High-Dimensional Feature Selection of Microarrays [J]. Journal of System Simulation. 2023, 35(03): 515-524.
[6] Ma Yunpeng. Machine Learning-based Estimation of Microbial Dissolved Organic Carbon Content [J]. Advances in Biotechnology. 2023, 13(04): 645-653.

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