# Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier

arXiv: https://arxiv.org/abs/1910.09131 · HTML: https://mlsanity.com/paper/1910.09131/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [DeepBF: Malicious URL detection using Learned Bloom Filter and Evolutionary Deep Learning](https://mlsanity.com/paper/2103.12544/) (2021)
- [Learned Sorted Table Search and Static Indexes in Small Model Space](https://mlsanity.com/paper/2107.09480/) (2021)
- [On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters](https://mlsanity.com/paper/2112.06563/) (2021)
- [A Survey of Learned Indexes for the Multi-dimensional Space](https://mlsanity.com/paper/2403.06456/) (2024)
- [On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes](https://mlsanity.com/paper/2203.14777/) (2022)
- [Daisy Bloom Filters](https://mlsanity.com/paper/2205.14894/) (2022)
- [Proteus: A Self-Designing Range Filter](https://mlsanity.com/paper/2207.01503/) (2022)
- [Context Compaction Theory](https://mlsanity.com/paper/2608.01326/) (2026)
- [Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions](https://mlsanity.com/paper/2603.00537/) (2026)
- [A Critical Analysis of Classifier Selection in Learned Bloom Filters](https://mlsanity.com/paper/2211.15565/) (2022)
- [From Specific to Generic Learned Sorted Set Dictionaries: A Theoretically Sound Paradigm Yelding Competitive Data Structural Boosters in Practice](https://mlsanity.com/paper/2309.00946/) (2023)
- [Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejection](https://mlsanity.com/paper/2502.03696/) (2025)
- [Learned Static Function Data Structures](https://mlsanity.com/paper/2510.27588/) (2025)
- [Optimized Learned Count-Min Sketch](https://mlsanity.com/paper/2512.12252/) (2025)
- [Learning Filters with Certainty](https://mlsanity.com/paper/2606.22786/) (2026)

## Abstract (arXiv)

Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier. However, such learned Bloom filter does not take full advantage of the predicted probability scores. We proposed new algorithms that generalize the learned Bloom filter by using the complete spectrum of the scores regions. We proved our algorithms have lower False Positive Rate (FPR) and memory usage compared with the existing approaches to learned Bloom filter. We also demonstrated the improved performance of our algorithms on real-world datasets.