Learned Bloom Filters, i.e., models induced from data via machine learning
techniques and solving the approximate set membership problem, have recently
been introduced with the aim of enhancing the performance of standard Bloom
Filters, with special focus on space occupancy. Unlike in the classical case,
the "complexity" of the data used to build the filter might heavily impact on
its performance. Therefore, here we propose the first in-depth analysis, to the
best of our knowledge, for the performance assessment of a given Learned Bloom
Filter, in conjunction with a given classifier, on a dataset of a given
classification complexity. Indeed, we propose a novel methodology, supported by
software, for designing, analyzing and implementing Learned Bloom Filters in
function of specific constraints on their multi-criteria nature (that is,
constraints involving space efficiency, false positive rate, and reject time).
Our experiments show that the proposed methodology and the supporting software
are valid and useful: we find out that only two classifiers have desirable
properties in relation to problems with different data complexity, and,
interestingly, none of them has been considered so far in the literature. We
also experimentally show that the Sandwiched variant of Learned Bloom filters
is the most robust to data complexity and classifier performance variability,
as well as those usually having smaller reject times. The software can be
readily used to test new Learned Bloom Filter proposals, which can be compared
with the best ones identified here.

提出一种用于设计、分析和实现 “Learned Bloom Filters” 的新方法，并结合分类器和数据集进行了性能评估，我们的实验表明，这种方法和软件可以有效地帮助确定最佳的分类器和最适合不同数据复杂度问题的 “Learned Bloom Filters” 的类型。

学习型布隆过滤器中分类器选择的关键分析

A Critical Analysis of Classifier Selection in Learned Bloom Filters

We provide a simple method for improving the performance of the recently
introduced learned Bloom filters, by showing that they perform better when the
learned function is sandwiched between two Bloom filters.

本文提出一种简单方法用于提高学习型 Bloom 过滤器的性能，即将学习功能夹在两个 Bloom 过滤器之间以实现更好的性能。