Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The $k$-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of $k$ for specific data distribution, thus demanding expensive computational efforts. This paper proposes a $k$-Nearest Neighbors classifier that bypasses the need to define the value of $k$. The model computes the $k$ value adaptively considering the data distribution of the training set. We compared the proposed model against the standard $k$-Nearest Neighbors classifier and two parameterless versions from the literature. Experiments over 11 public datasets confirm the robustness of the proposed approach, for the obtained results were similar or even better than its counterpart versions.

翻译：对机器学习模型中最低参数设置的需求是可取的,以避免耗费时间的优化过程。 $k$- nearneghearbors是许多问题中使用的最有效和最直截了当的模式之一。 尽管其性能众所周知,但它要求具体数据分配需要价值为$k$,从而需要昂贵的计算努力。本文提议了一个$k$- nearbors分类器,该分类器绕过确定$k$价值的需要。模型根据培训数据集的数据分配情况,根据适应性计算了$k$值。我们比较了拟议模型与标准$k$- Nearest nearbors分类器和文献中两个无参数版本。 超过11个公共数据集的实验证实了拟议方法的稳健性,因为所获得的结果与对应版本相似或甚至更好。