arXiv:2512.01097v1 Announce Type: new 
Abstract: 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.

تم تقديم إطار تصنيف جديد يسمى Smart Bayes، والذي يدمج ميزات توليدية قائمة على نسبة الاحتمالية في مصنف تمييزي على طراز الانحدار اللوجستي. تتيح هذه الطريقة وجود معاملات مدفوعة بالبيانات على ميزات نسبة الكثافة، مما يعزز فصل الفئات مقارنة بالطرق التقليدية مثل Naive Bayes والانحدار اللوجستي.

Se ha introducido un nuevo marco de clasificación llamado Smart Bayes, que integra características generativas basadas en la razón de verosimilitud en un clasificador discriminativo de estilo regresión logística. Este enfoque permite coeficientes impulsados por datos en características de razón de densidad, mejorando así la separación de clases en comparación con métodos tradicionales como Naive Bayes y la regresión logística.

Un nouveau cadre de classification nommé Smart Bayes a été introduit, intégrant des caractéristiques génératives basées sur le rapport de vraisemblance dans un classificateur discriminatif de type régression logistique. Cette approche permet des coefficients basés sur les données pour les caractéristiques de rapport de densité, améliorant ainsi la séparation des classes par rapport aux méthodes traditionnelles comme Naive Bayes et la régression logistique.

A new classification framework named Smart Bayes has been introduced, which integrates likelihood-ratio-based generative features into a logistic-regression-style discriminative classifier. This approach allows for data-driven coefficients on density-ratio features, enhancing class separation compared to traditional methods like Naive Bayes and logistic regression.

Discriminative classification with generative features: bridging Naive Bayes and logistic regression

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