标题： 具有单调性约束的模糊k-最近邻：向单调噪声的鲁棒性迈进 显示英文标题

标题： Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise

摘要： 本文提出了一种基于模糊k-最近邻的分类模型，该模型考虑了单调约束，称为单调模糊k-NN（MonFkNN）。 现实世界的数据集由于类别噪声往往不符合单调约束。 MonFkNN引入了一种新的模糊隶属度计算方法，在不需要重新标记的情况下提高了对单调噪声的鲁棒性。 我们的方案设计为能够适应所解决的不同问题需求。 在多个实验研究中，我们展示了在保持与比较方法获得的最佳单调程度相匹配的情况下，准确性的显著提升。 我们还表明，在存在大量类别噪声的情况下，MonFkNN在实践中相比单调k-NN表现出更好的性能。 显示英文摘要

摘要： This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do not comply with monotonic constraints due to class noise. MonFkNN incorporates a new calculation of fuzzy memberships, which increases robustness against monotonic noise without the need for relabeling. Our proposal has been designed to be adaptable to the different needs of the problem being tackled. In several experimental studies, we show significant improvements in accuracy while matching the best degree of monotonicity obtained by comparable methods. We also show that MonFkNN empirically achieves improved performance compared with Monotonic k-NN in the presence of large amounts of class noise.