A Sphere-Description-Based Approach for Multiple-Instance Learning

Multiple-instance learning (MIL) is a generalization of supervised learning which addresses the classification of bags. Similar to traditional supervised learning, most of the existing MIL work is proposed based on the assumption that a representative training set is available for a proper learning of the classifier. That is to say, the training data can appropriately describe the distribution of positive and negative data in the testing set. However, this assumption may not be always satisfied. In real-world MIL applications, the negative data in the training set may not sufficiently represent the distribution of negative data in the testing set. Hence, how to learn an appropriate MIL classifier when a representative training set is not available becomes a key challenge for real-world MIL applications. To deal with this problem, we propose a novel Sphere-Description-Based approach for Multiple-Instance Learning (SDB-MIL). SDB-MIL learns an optimal sphere by determining a large margin among the instances, and meanwhile ensuring that each positive bag has at least one instance inside the sphere and all negative bags are outside the sphere. Enclosing at least one instance from each positive bag in the sphere enables a more desirable MIL classifier when the negative data in the training set cannot sufficiently represent the distribution of negative data in the testing set. Substantial experiments on the benchmark and real-world MIL datasets show that SDB-MIL obtains statistically better classification performance than the MIL methods compared.