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Machine Learning Theory and Practice, 2023, 4(1); doi: 10.38007/ML.2023.040103.

A Dropout Optimization Algorithm to Prevent Overfitting in Machine Learning


Xiuming Wu

Corresponding Author:
Xiuming Wu

Finance Department, Criminal investigation Police University of China, Shenyang 110035, Liaoning, China


In the computer field, data encryption has always been a hot research topic, and TreScriptic algorithm has become the object of attention and research because of its simple parameters, clear classification and other advantages. This paper mainly introduces the web server which is built based on different models and has good performance, does not affect the system performance, has strong portability, and can be widely used in most occasions. This paper proposes a genetic NSBP neural network based on evolutionary algorithm, which is over fitting multi-dimensional and has global optimization characteristics. In this algorithm, parameters are regarded as input variables rather than directly interacting with output samples. Then this paper designs the Dropout optimization algorithm process and tests the performance. The test results show that the performance of the Dropout optimization algorithm based on machine learning to prevent over fitting takes about 5 seconds to process data, and the time to prevent over fitting is at least 3 seconds. This method can improve the biological signal processing effect of the over fitting.


Machine Learning, Over Fitting, Dropout Optimization Algorithm, Algorithm Research

Cite This Paper

Xiuming Wu. A Dropout Optimization Algorithm to Prevent Overfitting in Machine Learning. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 18-26. https://doi.org/10.38007/ML.2023.040103.


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