.png){kind=icon}
返回
Fuzzy rough unlearning model for feature selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijar.2023.109102.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In 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 It is a challenging problem, called machine unlearning, to efficiently forget the information of those outdated data from historical models.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 Yet, it is under study to remove outdated data from learned feature selection in fuzzy rough philosophy.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. Specifically, the outdated information is first identified in a compact set, called unlearning set, that remarkably shrinks the search space of feature selection. Then, the unlearning mechanisms containing two main theorems are proposed by leveraging unlearning set, followed by a feature selection unlearning algorithm. Theoretical analyses verify that the proposed unlearning algorithm is equivalent to the traditional algorithm retraining on the remaining data with the beginning of historical results. Experimentally, extensive results on 20 datasets demonstrate that our proposed unlearning methods perform effectively with remarkably less time cost. For the source code of ARU, please refer to: https://github.com/ yuxin370/ARU.
Keyword:
Fuzzy rough sets
Unlearning
Feature selection
Positive region
期刊
IF:
3
论文数:
3.0K
被引数:
5.1K
机构
引用论文
分享收藏A Group Incremental Approach to Feature Selection Applying Rough Set Technique应用粗糙集技术的组增量式特征选择方法
PREAI
分享收藏分享收藏Fracture risk with use of liver enzyme inducing antiepileptic drugs in people with active epilepsy: Cohort study using the General Practice Research Database.png)
Seizure
OAAI
SeizureIF0
OAAI分享收藏
查看更多内容