Research and Application of Random Forest Model Based on Genetic Algorithm Optimisation

Lixiang Yu; Wenkui Wu

doi:10.62051/1w92yr11

Authors

Lixiang Yu
Wenkui Wu

DOI:

https://doi.org/10.62051/1w92yr11

Keywords:

Random Forest, Prediction Model, Genetic Algorithm, Big Data.

Abstract

In this study, an in-depth analysis of the relationship between infant behavioural characteristics and mothers' physical and psychological indicators was conducted by integrating a random forest model optimised by a genetic algorithm. A mathematical model of treatment cost and health improvement rate was also established, which provides a scientific basis and technical support for infant behaviour analysis and individualized intervention strategies.

Downloads

Download data is not yet available.

References

Naghibi S A, Ahmadi K, Daneshi A. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping[J]. Water Resources Management, 2017, 31: 2761-2775.

Elyan E, Gaber M M. A genetic algorithm approach to optimising random forests applied to class engineered data[J]. Information sciences, 2017, 384: 220-234.

Ansótegui C, Malitsky Y, Samulowitz H, et al. Model-Based Genetic Algorithms for Algorithm Configuration[C]//IJCAI. 2015: 733-739.

Cerrada M, Zurita G, Cabrera D, et al. Fault diagnosis in spur gears based on genetic algorithm and random forest[J]. Mechanical Systems and Signal Processing, 2016, 70: 87-103.

Wang H, Jin Y. A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems[J]. IEEE transactions on cybernetics, 2018, 50(2): 536-549.

Zhu E, Chen Z, Cui J, et al. MOE/RF: a novel phishing detection model based on revised multiobjective evolution optimization algorithm and random forest[J]. IEEE Transactions on Network and Service Management, 2022, 19(4): 4461-4478.