As a space-efficient probability structure,filters can efficiently solve approximate set membership queries.In recent years,with the development of machine learning technology,some learning-based filters have exceeded traditional filters in per-formance.These learning-based filters propose to consider data distribution information and treat set membership queries as a bi-nary classification problem,achieving superior performance compared to traditional filters.Inspired by this,the research field of learning-based filters has progressed rapidly,and several variants have emerged.However,there is still a lack of a systematic re-view and comparison of recent related work.In order to fill this gap,this paper comprehensively reviews recent related work on learning-based filters,analyzes their structure design and theoretical analysis,and predicts the future development direction.
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