BOOMER — An algorithm for learning gradient boosted multi-label classification rules - Rapp, Michael (Software impacts, 2021)
Multi-label classification is concerned with the assignment of sets of labels to individual data points. Due to its diverse real-world applications, e.g., the annotation of text documents with topics, it has become a well-established field of machine learning research. Compared to traditional classification, where classes are mutually exclusive, multi-label classification comes with interesting challenges, most prominently the requirement to take dependencies between labels into account. In this work, we present a modular and customizable implementation of BOOMER – an algorithm for learning gradient boosted multi-label classification rules – that can flexibly be adjusted to different use cases and requirements. •BOOMER is an algorithm for learning gradient boosted multi-label classification rules.•The goal of multi-label classification is the automatic assignment of sets of labels to individual data points.•BOOMER enables to optimize decomposable and non-decomposable loss functions.•The implementation incorporates several optimizations and approximation techniques to be able to deal with large datasets.•Gradient-based Label Binning can be used to form groups of similar labels.