Research on random forest algorithm based on unbalanced data

Weibing Feng; Guohui Lu; Xiaogang Xia

doi:10.1117/12.2646339

23 August 2022 Research on random forest algorithm based on unbalanced data

Weibing Feng, Guohui Lu, Xiaogang Xia

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301X (2022) https://doi.org/10.1117/12.2646339
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

As one of the most popular classification algorithms, random forest has been widely used in various fields of life. It not only has a relatively perfect theoretical background, but also has a good performance in data classification. However, the classification and prediction of unbalanced data sets is still difficult. In order to solve this problem, a classification and prediction model of unbalanced data sets based on adasyn-rf is proposed. Firstly, adasyn algorithm is used to deal with unbalanced data sets, and then RF model prediction is constructed. In order to illustrate the superiority of the model, comparative experiments are added. That is, the original data set and the data set processed by smote, boundary smote and adasyn are combined with two prediction models: random forest model and xgboost model. The experimental results show that the classification and prediction effect of unbalanced data sets based on adasyn-rf model is the best.

Citation Download Citation

Weibing Feng, Guohui Lu, and Xiaogang Xia "Research on random forest algorithm based on unbalanced data", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301X (23 August 2022); https://doi.org/10.1117/12.2646339

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