An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
It addresses imbalanced data classification, particularly for tasks like tax fraud detection, but is incremental as it modifies an existing method.
The paper tackles learning from imbalanced data by adjusting a Nearest-Neighbor algorithm to optimize the F-Measure, showing effectiveness on public datasets and a large-scale tax fraud detection task, with best performance when combined with state-of-the-art sampling methods.
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.