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Cloud Based Error Correction and Big Data Mining for Computer Machine Learning Algorithms

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DOI: 10.23977/jeis.2025.100119 | Downloads: 5 | Views: 281

Author(s)

Ziyi Huang 1

Affiliation(s)

1 School of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, 110035, Liaoning, China

Corresponding Author

Ziyi Huang

ABSTRACT

With the advent of the Big Data (BD) era, Machine Learning (ML) algorithms have been widely applied in various industries. Among them, ML algorithms are mainly trained on computers. Due to various problems that may arise during the training process, ML algorithms produce various errors and cannot achieve good results. This article analyzed a cloud based error correction system and BD mining system for computer ML algorithms. This system could effectively solve various problems that arose in ML algorithms, and its effectiveness was verified through experiments. By analyzing cloud error correction technology and BD mining technology, a cloud error correction system and BD mining system were constructed, and ML algorithms were used to verify the feasibility of the system in terms of average running time and accuracy. Through experimental research, it was found that the accuracy of using ML algorithms was 5.8% higher than using neural network algorithms. There were currently multiple ways to process BD, and using ML patterns to optimize BD processing was a relatively effective approach. Simulation experiments showed that the ML algorithm proposed in this paper had higher accuracy overall than neural network algorithms, thus making it an effective and practical optimization algorithm.

KEYWORDS

Machine Learning Algorithms, Cloud Based Error Correction System, Big Data Mining System, Data Preprocessing

CITE THIS PAPER

Ziyi Huang, Cloud Based Error Correction and Big Data Mining for Computer Machine Learning Algorithms. Journal of Electronics and Information Science (2025) Vol. 10: 145-154. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100119.

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