  A key element of any machine learning algorithm is the use of a function that
measures the dis/similarity between data points. Given a task, such a function
can be optimized with a metric learning algorithm. Although this research field
has received a lot of attention during the past decade, very few approaches
have focused on learning a metric in an imbalanced scenario where the number of
positive examples is much smaller than the negatives. Here, we address this
challenging task by designing a new Mahalanobis metric learning algorithm (IML)
which deals with class imbalance. The empirical study performed shows the
efficiency of IML.
