论文标题
积极特征值优先级分层冗余消除了树的增强型幼稚贝叶斯分类器的分层特征空间
Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces
论文作者
论文摘要
分层冗余消除了树木增强的幼稚贝叶斯（HRE-tan）分类器是一种半名称的贝叶斯模型，它可以学习一种类型的无层次冗余树状特征表示，以估计数据分布。在这项工作中，我们提出了两种新型的积极特征值优先级的分层冗余，消除了树木增强的幼稚贝叶斯分类器，这些分类器专注于带有正实例值的特征。与常规的HRE-TAN分类器相比，这两种新提出的方法应用于28个现实世界的生物信息学数据集，显示出更好的预测性能。
The Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN) classifier is a semi-naive Bayesian model that learns a type of hierarchical redundancy-free tree-like feature representation to estimate the data distribution. In this work, we propose two new types of positive feature values prioritized hierarchical redundancy eliminated tree augmented naive Bayes classifiers that focus on features bearing positive instance values. The two newly proposed methods are applied to 28 real-world bioinformatics datasets showing better predictive performance than the conventional HRE-TAN classifier.
