# On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters

arXiv: https://arxiv.org/abs/2112.06563 · HTML: https://mlsanity.com/paper/2112.06563/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Algorithms with Predictions](https://mlsanity.com/paper/2006.09123/) (2020)
- [A Model for Learned Bloom Filters, and Optimizing by Sandwiching](https://mlsanity.com/paper/1901.00902/) (2019)
- [RadixSpline: A Single-Pass Learned Index](https://mlsanity.com/paper/2004.14541/) (2020)
- [Benchmarking Learned Indexes](https://mlsanity.com/paper/2006.12804/) (2020)
- [Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier](https://mlsanity.com/paper/1910.09131/) (2019)
- [Partitioned Learned Bloom Filter](https://mlsanity.com/paper/2006.03176/) (2020)
- [A Critical Analysis of Recursive Model Indexes](https://mlsanity.com/paper/2106.16166/) (2021)
- [Compression strategies and space-conscious representations for deep neural networks](https://mlsanity.com/paper/2007.07967/) (2020)

## Built on by (most cited first, 4 of 4)

- [On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes](https://mlsanity.com/paper/2203.14777/) (2022)
- [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)

## Abstract (arXiv)

Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available. We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.