MILD: Multiple-Instance Learning via Disambiguation

In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set are associated with bags rather than instances. A bag is labeled positive if at least one of its instances is positive; otherwise, the bag is labeled negative. Since a positive bag may contain some negative instances in addition to one or more positive instances, the true labels for the instances in a positive bag may or may not be the same as the corresponding bag label and, consequently, the instance labels are inherently ambiguous. In this paper, we propose a very efficient and robust MIL method, called MILD (Multiple-Instance Learning via Disambiguation), for general MIL problems. First, we propose a novel disambiguation method to identify the true positive instances in the positive bags. Second, we propose two feature representation schemes, one for instance-level classification and the other for bag-l...

• Type: Journal
• Year: 2010
• Where: TKDE
• Authors: Wu-Jun Li, Dit-Yan Yeung
• Keywords: Mil Problem, Positive Bags, Positive Instances, TKDE 2010