  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 a 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 to
support MIL. The first level of the tree represents the bags, and the second
level represents the instances belonging to each bag, allowing the user to
understand the data in an intuitive way. In addition, we propose two new
instance selection methods for MIL, which help the user to improve the model
even further. Our methods are also able to 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 - composed by the prototype instances. Experimental
results validate the effectiveness of our approach, showing that visual mining
by MILTree can help users in exploring and improving models in MIL scenarios,
and that our instance selection methods over-perform current available
alternatives in most cases.
