Multiple instance learning for sparse positive bags

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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Razvan C. Bunescu, Raymond J. Mooney
• Keywords: ICML 2007, Machine Learning, Positive Bags, Positive Instances, State-of-the-art Mil Methods