Multiple-instance Learning (MIL) is a new paradigm
of supervised learning that deals with the classification of
bags. Each bag is presented as a collection of instances
from which features are extracted. In MIL, we have usually
confronted with a large instance space for even moderately
sized data sets since each bag may contain many
instances. Hence it is important to design efficient instance
pruning and selection techniques to speed up the learning
process without compromising on the performance. In this
paper, we address the issue of instance selection in multiple
instance learning and propose the IS-MIL, an Instance Selection
framework for MIL, to tackle large-scale MIL problems.
IS-MIL is based on an alternative optimisation framework
by iteratively repeating the steps of instance selection/
updating and classifier learning, which is guaranteed
to converge. Experimental results demonstrate the utility
and efficiency of the proposed approach compared to the
alterna...