An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data

Abstract

In this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm. In this setting, the minority examples typically belong to the class of interest requiring the optimization of specific criteria, like the F-Measure. Based on simple geometrical ideas, we introduce an algorithm that reweights the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN algorithm. We provide a theoretical justification about the weighting scheme needed to reduce the False Negative rate while controlling the number of False Positives. We perform an extensive experimental study on many public imbalanced datasets, but also on large scale non public data from the French Ministry of Economy and Finance on a tax fraud detection task, showing that our method is very effective and, interestingly, yields the best performance when combined with state of the art sampling methods.

A Nearest Neighbor Algorithm for Imbalanced Classification

“…This paper improves substantially on our previous work [33], both with increased details and new algorithmic and experimental contributions: (i) we show an explicit link between the proposed method, called γk−NN, and cost-sensitive learning, (ii) we present a local version of our method that uses clustering to adapt the parameters to the different regions of the input space, and (iii) we rework and extend the experimental study to incorporate new performance measures and to give a qualitative analysis on the well-known image dataset MNIST.…”