论文标题
分层依赖性限制了树的增强幼稚贝叶斯分类器的分层特征空间
Hierarchical Dependency Constrained Tree Augmented Naive Bayes Classifiers for Hierarchical Feature Spaces
论文作者
论文摘要
树增强幼稚的贝叶斯（TAN）分类器是一种概率图形模型，该模型构建了单亲依赖树以估计数据的分布。在这项工作中，我们提出了两种新型的基于分层的基于层次依赖性的树木增强幼稚的贝叶斯算法，即hie-tan和hie-tan-lite。两种方法都利用了特征作为一种约束类型之间的预定义的亲子（普遍化特殊化）关系，以了解特征之间依赖性的树表示，而后者则进一步消除了分类器学习阶段的层次冗余。实验结果表明，与其他几种分层依赖性分类算法相比，HIE-TAN成功获得了更好的预测性能，并且通过消除层次冗余，其预测性能得到了进一步提高，如Hie-Tan-Lite所获得的较高准确性所表明的那样。
The Tree Augmented Naive Bayes (TAN) classifier is a type of probabilistic graphical model that constructs a single-parent dependency tree to estimate the distribution of the data. In this work, we propose two novel Hierarchical dependency-based Tree Augmented Naive Bayes algorithms, i.e. Hie-TAN and Hie-TAN-Lite. Both methods exploit the pre-defined parent-child (generalisation-specialisation) relationships between features as a type of constraint to learn the tree representation of dependencies among features, whilst the latter further eliminates the hierarchical redundancy during the classifier learning stage. The experimental results showed that Hie-TAN successfully obtained better predictive performance than several other hierarchical dependency constrained classification algorithms, and its predictive performance was further improved by eliminating the hierarchical redundancy, as suggested by the higher accuracy obtained by Hie-TAN-Lite.
