Multi-instance tree learning

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 outperforms the existing multi-instance decision tree learners.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Hendrik Blockeel, David Page, Ashwin Srinivasan
• Keywords: Decision Tree Learners, Dietterich Et Al, ICML 2005, Machine Learning, Multi-instance Tree Learners