Section: New Results

Fast imbalanced binary classification: a moment-based approach

Participant : Edouard Grave.

In this paper, we consider the problem of imbalanced binary classification in which the number of negative examples is much larger than the number of positive examples. The two mainstream methods to deal with such problems are to assign different weights to negative and positive points or to subsample points from the negative class. In this paper, we propose a different approach: we represent the negative class by the two first moments of its probability distribution (the mean and the covariance), while still modeling the positive class by individual examples. Therefore, our formulation does not depend on the number of negative examples, making it suitable to highly imbalanced problems and scalable to large datasets. We demonstrate empirically, on a protein classification task and a text classification task, that our approach achieves similar statistical performance than the two mainstream approaches to imbalanced classification problems, while being more computationally efficient. (in collaboration with Laurent El Ghaoui, U.C. Berkeley)