BOOMER — An algorithm for learning gradient boosted multi-label classification rules
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 classi...
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| Published in: | Software impacts Vol. 10; p. 100137 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.11.2021
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| Subjects: | |
| ISSN: | 2665-9638, 2665-9638 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 2665-9638 2665-9638 |
| DOI: | 10.1016/j.simpa.2021.100137 |