Adaptive $k$-nearest neighbor classifier based on the local estimation of the shape operator

TL;DR

This paper introduces an adaptive $k$-NN classifier that adjusts neighborhood size based on local data curvature, improving accuracy especially with limited data by better capturing data shape.

Contribution

The paper proposes a novel $kK$-NN algorithm that estimates local curvature via the shape operator to adaptively select neighborhood size, enhancing classification performance.

Findings

Outperforms standard $k$-NN in balanced accuracy.

More effective with limited training data.

Provides better data shape approximation.

Abstract

The $k$-nearest neighbor ($k$-NN) algorithm is one of the most popular methods for nonparametric classification. However, a relevant limitation concerns the definition of the number of neighbors $k$. This parameter exerts a direct impact on several properties of the classifier, such as the bias-variance tradeoff, smoothness of decision boundaries, robustness to noise, and class imbalance handling. In the present paper, we introduce a new adaptive $k$-nearest neighbours ($kK$-NN) algorithm that explores the local curvature at a sample to adaptively defining the neighborhood size. The rationale is that points with low curvature could have larger neighborhoods (locally, the tangent space approximates well the underlying data shape), whereas points with high curvature could have smaller neighborhoods (locally, the tangent space is a loose approximation). We estimate the local Gaussian curvature by computing an approximation to the local shape operator in terms of the local covariance matrix as well as the local Hessian matrix. Results on many real-world datasets indicate that the new $kK$-NN algorithm yields superior balanced accuracy compared to the established $k$-NN method and also another adaptive $k$-NN algorithm. This is particularly evident when the number of samples in the training data is limited, suggesting that the $kK$-NN is capable of learning more discriminant functions with less data considering many relevant cases.