Research on Imbalanced Classification Problem Based on Optimal Random Forest Algorithm

Yue Shan, Liu Hui, He Zheng, Yong Qiaoling, Wang Yali
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:383-392, 2024.

Abstract

In order to solve the binary classification problem of imbalanced data, an optimal random forest algorithm GWORF (Grey Wolf Optimizer Random Forest) is pro-posed. The algorithm first uses BLSMOTE (BorderLine SMOTE) technology to oversample the imbalanced data set to make the positive and negative data equivalent, and then uses the Grey Wolf optimization algorithm to calculate the optimal parameters, and then puts the calculated optimal parameters into the forest for modeling training. Through testing on four imbalanced data sets, the effectiveness of the GWORF algorithm in the study of imbalanced binary classification problems is verified.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-shan24a, title = {Research on Imbalanced Classification Problem Based on Optimal Random Forest Algorithm}, author = {Shan, Yue and Hui, Liu and Zheng, He and Qiaoling, Yong and Yali, Wang}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {383--392}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/shan24a/shan24a.pdf}, url = {https://proceedings.mlr.press/v245/shan24a.html}, abstract = {In order to solve the binary classification problem of imbalanced data, an optimal random forest algorithm GWORF (Grey Wolf Optimizer Random Forest) is pro-posed. The algorithm first uses BLSMOTE (BorderLine SMOTE) technology to oversample the imbalanced data set to make the positive and negative data equivalent, and then uses the Grey Wolf optimization algorithm to calculate the optimal parameters, and then puts the calculated optimal parameters into the forest for modeling training. Through testing on four imbalanced data sets, the effectiveness of the GWORF algorithm in the study of imbalanced binary classification problems is verified.} }
Endnote
%0 Conference Paper %T Research on Imbalanced Classification Problem Based on Optimal Random Forest Algorithm %A Yue Shan %A Liu Hui %A He Zheng %A Yong Qiaoling %A Wang Yali %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-shan24a %I PMLR %P 383--392 %U https://proceedings.mlr.press/v245/shan24a.html %V 245 %X In order to solve the binary classification problem of imbalanced data, an optimal random forest algorithm GWORF (Grey Wolf Optimizer Random Forest) is pro-posed. The algorithm first uses BLSMOTE (BorderLine SMOTE) technology to oversample the imbalanced data set to make the positive and negative data equivalent, and then uses the Grey Wolf optimization algorithm to calculate the optimal parameters, and then puts the calculated optimal parameters into the forest for modeling training. Through testing on four imbalanced data sets, the effectiveness of the GWORF algorithm in the study of imbalanced binary classification problems is verified.
APA
Shan, Y., Hui, L., Zheng, H., Qiaoling, Y. & Yali, W.. (2024). Research on Imbalanced Classification Problem Based on Optimal Random Forest Algorithm. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:383-392 Available from https://proceedings.mlr.press/v245/shan24a.html.

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