Kovács, Edith Alice and Ország, Anna and Pfeifer, Dániel and Benczúr, András, ifj (2026) Generalized Naive Bayes. PATTERN RECOGNITION, 174. ISSN 0031-3203 10.1016/j.patcog.2025.112927

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Abstract

This paper introduces the Generalized Naive Bayes (GNB) structure as an extension of the Naive Bayes structure. We give two new greedy algorithms to find the possible closest GNB structure to the data in terms of minimizing Kullback-Leibler divergence between the probability distribution corresponding to the GNB structure and the data. Both algorithms focus on determining the best-fitting GNB structure based on the training data. The first algorithm adopts a greedy approach to find a good-fitting GNB probability distribution. Then, we introduce a second algorithm, which we prove finds the optimal (best-fitting) GNB probability distribution on the training set under a non-restrictive condition. Based on these algorithms, new feature importance scores and feature selection methods are introduced. The algorithms are compared from theoretical and practical points of view with other probabilistic graphical model-based algorithms.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Artificial Intelligence Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	09 Sep 2026 11:15
Last Modified:	09 Sep 2026 11:15
URI:	https://eprints.sztaki.hu/id/eprint/11140

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