Hierarchical Empirical-Bayes Naive Bayes: Minimax Smoothing and Calibration with AODE Extension
Score
75提出了自适应平滑的新方法，填补了朴素贝叶斯平滑超参数长期依赖人工设定的空白。
理论与实验结合紧密，推导严谨，实验覆盖广，结果可信度高。
方法可直接应用于表格数据分类和概率校准，且实现相对简单，具有较强落地潜力。
论文内容新颖，针对高基数数据这一当前实际需求，时效性较好。
来自arXiv预印本，作者与机构尚未确认但内容规范，属于可靠学术来源。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The paper combines rigorous theoretical analysis with large-scale empirical validation, yielding significant and reproducible results with high intelligence value.
AI commentary
「This paper appeals to both ML researchers and practitioners: adaptive smoothing addresses the long-standing hyperparameter issue in Naive Bayes, while the theoretical lower bound adds rigor. It is especially valuable for readers dealing with high-cardinality categorical features and probabilistic calibration.」
Quick overview
This paper addresses the limitations of fixed-strength smoothing in Naive Bayes classifiers on high-cardinality tabular data. The authors propose Hierarchical Empirical-Bayes Naive Bayes (HEB-NB), which learns Dirichlet prior concentrations via Type-II maximum likelihood, enabling adaptive smoothing and information sharing across classes while preserving closed-form inference. Theoretically, they establish a non-asymptotic l1 error bound matching the empirical-distribution minimax rate with vanishing bias, and a lower bound showing strict separation from Laplace smoothing. They also derive excess Bayes-risk and calibration error bounds. Empirically, across 31 UCI and OpenML benchmarks, HEB-NB achieves the best average Friedman rank on probabilistic metrics, with up to 22.1% log-loss reduction on high-cardinality datasets. The extended HEB-AODE consistently improves over vanilla AODE, and combining with mutual-information weighting reduces top-1 ECE by 41%-70%, demonstrating strong gains in probabilistic accuracy and calibration.
- Proposes HEB-NB, which adaptively learns Dirichlet prior concentrations via empirical Bayes, replacing fixed smoothing strength.
- Establishes a non-asymptotic ℓ1 error bound matching the empirical-distribution minimax rate, with strict separation from Laplace smoothing.
- Extends to HEB-AODE, transferring adaptive smoothing to relaxed first-order dependency structures with consistent gains.
- On 31 UCI/OpenML benchmarks, achieves up to 22.1% log-loss reduction on high-cardinality datasets and 41%-70% top-1 ECE reduction with mutual-information weighting.
Full text
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