What question did this study set out to answer?

The central aim is to address the limitations of K-dependence Bayesian classifiers by proposing adaptive models.

February 27, 2026IEEE Transactions on Neural Networks and Learning Systems0 citations

K-Free Dependence Bayesian Classifiers

KMKexin Meng HZHuan Zhang LJLiangxiao JIANG

Key Points

The central aim is to address the limitations of K-dependence Bayesian classifiers by proposing adaptive models.
Proposed K-free dependence Bayesian classifiers that allow flexible parent node counts for attributes.
Developed two versions of KFDB classifiers based on mean squared error and classification accuracy optimization.
Conducted experiments on 60 benchmark datasets to evaluate performance against classical models.
KFDB classifiers demonstrated significantly improved performance over the classical K-dependence Bayesian classifiers.
Both KFDB versions outperformed other state-of-the-art classifiers on tested datasets.

Abstract

As one of the most attractive Bayesian network classifiers (BNCs), the K-dependence Bayesian (KDB) classifier can effectively capture dependencies between attributes by allowing each of them to be conditioned on the class and, at most, K other attributes. However, as K becomes larger, its structural complexity greatly increases, which inevitably leads to a certain risk of overfitting. Moreover, when K is given, its structure is immutable, which dramatically limits the expression ability of the final model. To address these two issues, in this study, we propose K-free dependence Bayesian (KFDB) classifiers, which can learn an adaptive number of parent nodes for each attribute. To search its optimal structure, we sequentially evaluate the candidate submodels either by minimizing the mean squared error (MSE) or maximizing the classification accuracy (ACC), resulting in two versions denoted as KFDBMSE and KFDBACC, respectively. Experimental results on 60 benchmark datasets demonstrate that KFDB significantly outperforms the classical KDB and other state-of-the-art models.

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