A Critical Analysis of Classifier Selection in Learned Bloom Filters

TL;DR

This paper presents a comprehensive analysis and methodology for evaluating Learned Bloom Filters, emphasizing the impact of classifier choice and data complexity on performance, and introduces software for designing optimized filters.

Contribution

It introduces the first in-depth assessment method for Learned Bloom Filters considering classifier and data complexity, along with supporting software for multi-criteria optimization.

Findings

Only two classifiers perform well across different data complexities.

Sandwiched Learned Bloom Filter is most robust to data and classifier variability.

Software effectively tests and compares different Learned Bloom Filter configurations.

Abstract

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.