ML Anthology

Multi-Instance Tree Learning

Hendrik Blockeel, David Page, Ashwin Srinivasan
ICML 2005 pp. 57-64
doi:10.1145/1102351.1102359 /icml/2005/blockeel2005icml-multi/

Abstract

We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree learners in a few crucial, well-motivated details. Experiments on synthetic and real-life datasets confirm the beneficial effect of these differences and show that the resulting system out-performs the existing multi-instance decision tree learners.

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Cite

Text

Blockeel et al. "Multi-Instance Tree Learning." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102359

Markdown

[Blockeel et al. "Multi-Instance Tree Learning." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/blockeel2005icml-multi/) doi:10.1145/1102351.1102359

BibTeX

@inproceedings{blockeel2005icml-multi,
  title     = {{Multi-Instance Tree Learning}},
  author    = {Blockeel, Hendrik and Page, David and Srinivasan, Ashwin},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {57-64},
  doi       = {10.1145/1102351.1102359},
  url       = {https://mlanthology.org/icml/2005/blockeel2005icml-multi/}
}