A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes. (arXiv:2209.10095v1 [cs.LG])

In many classification models, data is discretized to better estimate its
distribution. Existing discretization methods often target at maximizing the
discriminant power of discretized data, while overlooking the fact that the
primary target of data discretization in classification is to improve the
generalization performance. As a result, the data tend to be over-split into
many small bins since the data without discretization retain the maximal
discriminant information. Thus, we propose a Max-Dependency-Min-Divergence
(MDmD) criterion that maximizes both the discriminant information …

applications arxiv bayes data divergence