Metric Learning from Imbalanced Data

Léo Gautheron (1) , Emilie Morvant (1) , Amaury Habrard (1) , Marc Sebban (1)

1 LHC - Laboratoire Hubert Curien

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.

Domaines: Machine Learning [stat.ML]

Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban. Metric Learning from Imbalanced Data. International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, Oregon, United States. ⟨hal-02276288⟩