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January 30, 2026Journal of Classification

An Extension of the Kullback–Leibler Divergence for the Application of Machine Learning Classification Algorithms to Hierarchical Cluster Analysis

P. Nikitas Efthymia Nikita

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Key Points

Performance improves with the extended kullback-leibler divergence metric, enhancing classification accuracy.
The new approach outperforms traditional clustering methods in terms of precision and recall metrics.
The framework integrates machine learning techniques with hierarchical cluster analysis.
The findings highlight potential improvements in data grouping for various machine learning applications.

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Implication

This analysis extends Kullback–Leibler divergence to enhance classification in hierarchical clusters, suggesting improved algorithm performance.

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Cite This Study

Nikitas et al. (2026) studied this question.

synapsesocial.com/papers/69a75ea1c6e9836116a296c0 https://doi.org/10.1007/s00357-025-09532-3

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