In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT)-hierarchical groupings of attribute values-to learn compact, comprehensible and accurate classifiers from data-including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the naïve Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. We also show that AVT-NBL is more efficient in its use of training data: AVT-NBL produces classifiers that outperform those produced by NBL using substantially fewer training examples.

中文翻译：

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从属性值分类法和数据中学习准确而简洁的朴素贝叶斯分类器。

在许多应用领域中，需要能够有效利用属性值分类法 (AVT)——属性值的分层分组——从数据（包括部分指定的数据）中学习紧凑、可理解和准确的分类器的学习算法。本文描述了 AVT-NBL，它是朴素贝叶斯学习器 (NBL) 的自然泛化，用于从 AVT 和数据中学习分类器。我们的实验结果表明，在具有不同百分比的部分指定值的广泛数据集上，AVT-NBL 能够生成比 NBL 生成的分类器更紧凑、更准确的分类器。我们还表明 AVT-NBL 在使用训练数据方面更有效：AVT-NBL 产生的分类器优于 NBL 产生的分类器，使用更少的训练样本。