一种最大化非平衡数据F-测度的调整最近邻算法
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
摘要：
在本文中，我们解决了具有挑战性的问题，学习不平衡的数据使用最近邻（NN）算法。在这种情况下，少数样本通常属于需要优化特定标准（如F-测度）的感兴趣类别。基于简单的几何思想，我们引入了一个算法，重新加权查询样本和任何积极的训练样本之间的距离。这导致Voronoi区域的修改，从而导致NN算法的决策边界的修改。我们提供了有关降低假阴性率同时控制假阳性数量所需的加权方案的理论依据。我们对许多公开的不平衡数据集进行了广泛的实验研究，还对法国经济和财政部的大规模非公开数据进行了税务欺诈检测任务，表明我们的方法非常有效，有趣的是，当与最先进的抽样方法相结合时，可以获得最佳性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
