We introduce Smart Bayes, a new classification framework that bridges generative and discriminative modeling by integrating likelihood-ratio-based generative features into a logistic-regression-style discriminative classifier. From the generative perspective, Smart Bayes relaxes the fixed unit weights of Naive Bayes by allowing data-driven coefficients on density-ratio features. From a discriminative perspective, it constructs transformed inputs as marginal log-density ratios that explicitly quantify how much more likely each feature value is under one class than another, thereby providing predictors with stronger class separation than the raw covariates. To support this framework, we develop a spline-based estimator for univariate log-density ratios that is flexible, robust, and computationally efficient. Through extensive simulations and real-data studies, Smart Bayes often outperforms both logistic regression and Naive Bayes. Our results highlight the potential of hybrid approaches that exploit generative structure to enhance discriminative performance.

翻译：我们提出Smart Bayes这一新的分类框架，通过将基于似然比的生成特征整合到逻辑回归风格的判别式分类器中，连接了生成式与判别式建模。从生成式视角看，Smart Bayes通过允许密度比特征具有数据驱动的系数，放宽了朴素贝叶斯中固定单位权重的限制。从判别式视角看，该框架将边际对数密度比构建为转换后的输入，这些输入显式量化了每个特征值在某一类别下相对于另一类别的可能性程度，从而提供了比原始协变量具有更强类别分离性的预测因子。为支持此框架，我们开发了一种基于样条的单变量对数密度比估计器，该估计器灵活、鲁棒且计算高效。通过大量模拟和实际数据研究，Smart Bayes通常优于逻辑回归和朴素贝叶斯。我们的结果凸显了利用生成式结构增强判别性能的混合方法的潜力。