Recently, many improved naive Bayes methods have been developed with enhanced discrimination capabilities. Among them, regularized naive Bayes (RNB) produces excellent performance by balancing the discrimination power and generalization capability. Data discretization is important in naive Bayes. By grouping similar values into one interval, the data distribution could be better estimated. However, existing methods including RNB often discretize the data into too few intervals, which may result in a significant information loss. To address this problem, we propose a semi-supervised adaptive discriminative discretization framework for naive Bayes, which could better estimate the data distribution by utilizing both labeled data and unlabeled data through pseudo-labeling techniques. The proposed method also significantly reduces the information loss during discretization by utilizing an adaptive discriminative discretization scheme, and hence greatly improves the discrimination power of classifiers. The proposed RNB+, i.e., regularized naive Bayes utilizing the proposed discretization framework, is systematically evaluated on a wide range of machine-learning datasets. It significantly and consistently outperforms state-of-the-art NB classifiers.

翻译：近年来，许多改进的朴素贝叶斯方法在判别能力方面取得了显著提升。其中，正则化朴素贝叶斯（RNB）通过平衡判别能力与泛化能力取得了优异的表现。数据离散化在朴素贝叶斯中至关重要。通过将相似值归并为一个区间，可以更准确地估计数据分布。然而，现有方法（包括RNB）通常将数据离散化为过少的区间，这可能导致显著的信息损失。为解决这一问题，我们提出了一种面向朴素贝叶斯的半监督自适应判别离散化框架，该框架通过伪标签技术同时利用标记数据与未标记数据，从而更准确地估计数据分布。所提方法还通过采用自适应判别离散化策略显著减少了离散化过程中的信息损失，进而大幅提升了分类器的判别能力。我们提出的RNB+（即采用所提离散化框架的正则化朴素贝叶斯）在广泛的机器学习数据集上进行了系统性评估。实验结果表明，RNB+显著且稳定地优于当前最先进的朴素贝叶斯分类器。