Classifier Chains for Multi-Label Classification
Abstract
The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
Cite
Text
Read et al. "Classifier Chains for Multi-Label Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_17Markdown
[Read et al. "Classifier Chains for Multi-Label Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/read2009ecmlpkdd-classifier/) doi:10.1007/978-3-642-04174-7_17BibTeX
@inproceedings{read2009ecmlpkdd-classifier,
title = {{Classifier Chains for Multi-Label Classification}},
author = {Read, Jesse and Pfahringer, Bernhard and Holmes, Geoffrey and Frank, Eibe},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {254-269},
doi = {10.1007/978-3-642-04174-7_17},
url = {https://mlanthology.org/ecmlpkdd/2009/read2009ecmlpkdd-classifier/}
}