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Selective Forgetting in Machine Learning and Beyond: A Survey
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DOI：10.1145/3796542.png)
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Abstract
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This survey investigates the multifaceted nature of selective forgetting in machine learning, drawing insights from neuroscientific research that posits forgetting as an adaptive function rather than a defect, enhancing the learning process and preventing overfitting. This survey focuses on the benefits of selective forgetting and its applications across various machine learning sub-fields that can help improve model performance and enhance data privacy. Moreover, the paper discusses current challenges, future directions, and ethical considerations regarding the integration of selective forgetting mechanisms into machine learning models. We present a comprehensive taxonomy that bridges diverse selective forgetting-related research in machine learning, systematically categorising approaches along key dimensions. Our work synthesises theories of forgetting from different knowledge areas to establish theoretical foundations for forgetting mechanisms in machine learning, providing a unified framework for understanding selective forgetting processes.
Keywords:
Forgetting
survey
machine learning
selective forgetting
reinforcement learning
learning enhancement
regularisation
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