CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning. Siblini, W., Kuntz, P., & Meyer, F. In Proceedings of the 35th International Conference on Machine Learning, pages 4664–4673, July, 2018. PMLR. ISSN: 2640-3498![CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Extreme Multi-label Learning (XML) considers large sets of items described by a number of labels that can exceed one million. Tree-based methods, which hierarchically partition the problem into small scale sub-problems, are particularly promising in this context to reduce the learning/prediction complexity and to open the way to parallelization. However, the current best approaches do not exploit tree randomization which has shown its efficiency in random forests and they resort to complex partitioning strategies. To overcome these limits, we here introduce a new random forest based algorithm with a very fast partitioning approach called CRAFTML. Experimental comparisons on nine datasets from the XML literature show that it outperforms the other tree-based approaches. Moreover with a parallelized implementation reduced to five cores, it is competitive with the best state-of-the-art methods which run on one hundred-core machines.
@inproceedings{siblini_craftml_2018,
title = {{CRAFTML}, an {Efficient} {Clustering}-based {Random} {Forest} for {Extreme} {Multi}-label {Learning}},
url = {https://proceedings.mlr.press/v80/siblini18a.html},
abstract = {Extreme Multi-label Learning (XML) considers large sets of items described by a number of labels that can exceed one million. Tree-based methods, which hierarchically partition the problem into small scale sub-problems, are particularly promising in this context to reduce the learning/prediction complexity and to open the way to parallelization. However, the current best approaches do not exploit tree randomization which has shown its efficiency in random forests and they resort to complex partitioning strategies. To overcome these limits, we here introduce a new random forest based algorithm with a very fast partitioning approach called CRAFTML. Experimental comparisons on nine datasets from the XML literature show that it outperforms the other tree-based approaches. Moreover with a parallelized implementation reduced to five cores, it is competitive with the best state-of-the-art methods which run on one hundred-core machines.},
language = {en},
urldate = {2021-10-18},
booktitle = {Proceedings of the 35th {International} {Conference} on {Machine} {Learning}},
publisher = {PMLR},
author = {Siblini, Wissam and Kuntz, Pascale and Meyer, Frank},
month = jul,
year = {2018},
note = {ISSN: 2640-3498},
pages = {4664--4673},
}
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