  Multiple-instance learning (MIL) is a paradigm of machine learning that aims
to classify a set (bag) of objects (instances), assigning labels only to the
bags. This problem is often addressed by selecting an instance to represent
each bag, transforming a MIL problem into standard supervised learning.
Visualization can be a useful tool to assess learning scenarios by
incorporating the users' knowledge into the classification process. Considering
that multiple-instance learning is a paradigm that cannot be handled by current
visualization techniques, we propose a multiscale tree-based visualization
called MILTree to support MIL problems. The first level of the tree represents
the bags, and the second level represents the instances belonging to each bag,
allowing users to understand the MIL datasets in an intuitive way. In addition,
we propose two new instance selection methods for MIL, which help users improve
the model even further. Our methods can handle both binary and multiclass
scenarios. In our experiments, SVM was used to build the classifiers. With
support of the MILTree layout, the initial classification model was updated by
changing the training set, which is composed of the prototype instances.
Experimental results validate the effectiveness of our approach, showing that
visual mining by MILTree can support exploring and improving models in MIL
scenarios and that our instance selection methods outperform the currently
available alternatives in most cases.
