让布隆过滤器输出判断置信度，提升机器学习系统的可靠性
Learning Filters with Certainty
- 用计数布隆过滤器的计数器量化正向判断的可信程度
- 在融合布隆过滤器与机器学习的系统中，置信度可降低误判率
- 适合需要高可靠性的网络缓存、异常检测等场景
基于哈希的数据结构如布隆过滤器广泛应用于网络系统，用于缓存、异常检测和机器学习流水线等任务。它们通常仅提供元素是否属于目标集合的二值判断，例如缓存内容。当因哈希冲突导致不确定性时，为避免漏检，系统会返回阳性结果。本文认为，这种判断结果的置信度本身也是有用信息。研究聚焦于计数布隆过滤器（Counting Bloom Filters, CBFs），该变体使用计数器而非比特位，支持插入与删除操作，并通过计数器提供额外信息，可用于估计阳性判断的确定性。本文展示了如何将这一置信度信号应用于结合布隆过滤器与机器学习模型的架构中。
原文摘要 · Abstract (English)
Hash-based data structures such as Bloom filters are widely used in network systems for tasks including caching, anomaly detection, and machine learning pipelines. They typically provide binary indications of whether an element belongs to a set of interest, e.g., the contents of a cache. When uncertainty arises due to hash collisions, a positive indication is returned to avoid false negatives. We argue that the certainty associated with such indications can itself be useful information. This work focuses on Counting Bloom Filters (CBFs), a Bloom-filter variant that maintains counters rather than bits. Besides supporting insertions and deletions, these counters provide additional information that can be used to estimate the certainty of positive membership indications. We show how this certainty signal can be exploited in architectures that combine Bloom Filters with machine learning (ML) models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。