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arXiv Machine Learning By \'Abrah\'am Papp, Botond Szil\'agyi, Edith Alice Kov\'acs

On Generalized Naive Bayes with Continuous Features

Read the original on arXiv Machine Learning →

The paper extends the Generalized Naive Bayes (GNB) model to handle continuous explanatory variables. It shows that GNB structure learning depends only on pair copulas of bivariate marginals and can be framed as a matroid, enabling greedy algorithms that minimize Kullback–Leibler divergence. Three model variants are explored—joint Gaussian, Gaussian copula with arbitrary marginals, and fully arbitrary copula and marginals—along with a GNB forest-based model reduction method and empirical comparisons to classical glass‑box classifiers.

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