We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other SVM-based MIL methods, our approach more directly enforces the desired constraint that at least one of the instances in a positive bag is positive. Using both artificial and real-world data, we experimentally demonstrate that our approach achieves greater accuracy than state-of-the-art MIL methods when positive bags are sparse, and performs competitively when they are not. In particular, our approach is the best performing method for image region classification.

PDF, PS

In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.

@inproceedings{bunescu:icml07, title={Multiple Instance Learning for Sparse Positive Bags}, author={Razvan C. Bunescu and Raymond J. Mooney}, booktitle={Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007)}, month={June}, address={Corvallis, OR}, url="http://www.cs.utexas.edu/users/ai-lab?bunescu:icml07", year={2007} }