Reachable Distance Function for KNN Classification

doi:10.1109/TKDE.2022.3185149

Reachable Distance Function for KNN Classification

2022-01-01

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DOI：10.1109/TKDE.2022.3185149

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Distance function is a main metrics of measuring the affinity between two data points in machine learning. Extant distance functions often provide unreachable distance values in real applications. This can lead to incorrect measure of the affinity between data points. This paper proposes a reachable distance function for KNN classification. The reachable distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the reachable distance between data points includes their class center distance and real distance. Its shape looks like Z, and we also call it a Z distance function. In this way, the affinity between data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the reachable distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification.

Keywords:

Euclidean distance

Covariance matrices

Probability distribution

Hamming distance

Training data

Machine learning

Chebyshev approximation

Distance functions

reachable distance

machine learning

KNN classification

Reachable Distance Function for KNN Classification

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