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Reachable Distance Function for KNN Classification
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DOI：10.1109/TKDE.2022.3185149.png)
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摘要
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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.
Keyword:
Euclidean distance
Covariance matrices
Probability distribution
Hamming distance
Training data
Machine learning
Chebyshev approximation
Distance functions
reachable distance
machine learning
KNN classification
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10.4
论文数:
6.9K
被引数:
3.2W
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