In the big data era, some data, becoming meaningless or illegal over time and space, need to be deleted from historical knowledge. It is a challenging problem, called machine unlearning, to efficiently forget the information of those outdated data from historical models. Some unlearning techniques have been proposed in loss-well-defined classification models, such as SVM, Random Forest, and Federated learning model. Yet, it is under study to remove outdated data from learned feature selection in fuzzy rough philosophy. To narrow this gap, we propose a fuzzy rough unlearning model for feature selection.
Windows 10 professional
Intel® Xeon® W-2145 CPU@ 3.70GHz
128 GB memory
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compile: make
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clean: make clean
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usage: test.exe start_of_conditionAttribute end_of_conditionAttribute decisionAttributePosition datasets initsize alpha foldsize >> result_path
eg: test.exe 1 19 20 demo 2310 0.3 5 >> result\demo\output.txt
we split the original datasets into six subsets and in each loop one subsets was removed.
@article{TANG2024109102, title = {Fuzzy rough unlearning model for feature selection}, journal = {International Journal of Approximate Reasoning}, volume = {165}, pages = {109102}, year = {2024}, issn = {0888-613X}, doi = {https://doi.org/10.1016/j.ijar.2023.109102}, url = {https://www.sciencedirect.com/science/article/pii/S0888613X23002335}, author = {Yuxin Tang and Suyun Zhao and Hong Chen and Cuiping Li and Junhai Zhai and Qiangjun Zhou}, keywords = {Fuzzy rough sets, Unlearning, Feature selection, Positive region} }