  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.

Machine Learning