论文标题
PL-KNN：无参数最近的邻居分类器
PL-kNN: A Parameterless Nearest Neighbors Classifier
论文作者
论文摘要
需要在机器学习模型中对最小参数设置的需求，以避免耗时的优化过程。 $ k $ - 最终的邻居是在许多问题中使用的最有效，最直接的模型之一。尽管具有众所周知的性能，但它仍需要特定数据分布的$ K $值，从而需要昂贵的计算工作。本文提出了一个$ k $ - 最终的邻居分类器，该分类器绕过定义$ k $的值的需求。考虑到训练集的数据分布，该模型计算$ K $值。我们将提出的模型与标准$ K $ - 最近的邻居分类器和文献中的两个无参数版本进行了比较。 11个公共数据集的实验证实了所提出方法的鲁棒性，因为所获得的结果相似甚至更好。
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.
