论文标题
模糊的K-Nearest邻居具有单调性约束：朝着单调噪音的稳健性发展
Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise
论文作者
论文摘要
本文提出了一个基于模糊的K-Neart邻居的新模型，用于单调约束，单调模糊K-NN（monfknn）。现实生活中的数据集通常由于类噪声而不符合单调约束。 Monfknn结合了模糊会员资格的新计算，这增加了对单调噪声的鲁棒性，而无需重新标记。我们的建议旨在适应解决问题的不同需求。在几项实验研究中，我们显示出准确性的显着提高，同时匹配通过可比方法获得的最佳单调性程度。我们还表明，与单调的K-NN相比，在存在大量的类噪声的情况下，MONFKNN的性能提高了。
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.
