Issue No. 01 - January (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.58
Wu-Jun Li , Hong Kong University of Science and Technology, Hong Kong
Dit-Yan Yeung , Hong Kong University of Science and Technology, Hong Kong
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 Multiple-Instance Learning via Disambiguation (MILD), 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-level classification, to convert the MIL problem into a standard single-instance learning (SIL) problem that can be solved by well-known SIL algorithms, such as support vector machine. Third, an inductive semi-supervised learning method is proposed for MIL. We evaluate our methods extensively on several challenging MIL applications to demonstrate their promising efficiency, robustness, and accuracy.
Multiple-instance learning, learning from ambiguity, CBIR, object recognition, co-training, drug activity prediction.
W. Li and D. Yeung, "MILD: Multiple-Instance Learning via Disambiguation," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 76-89, 2009.