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Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification

机译：分级多标签分类中的强制性叶节点预测

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摘要

In hierarchical classification, the output labels reside on a tree- or directed acyclic graph (DAG)-structured hierarchy. On testing, the prediction paths of a given test example may be required to end at leaf nodes of the label hierarchy. This is called mandatory leaf node prediction (MLNP) and is particularly useful, when the leaf nodes have much stronger semantic meaning than the internal nodes. However, while there have been a lot of MLNP methods in hierarchical multiclass classification, performing MLNP in hierarchical multilabel classification is difficult. In this paper, we propose novel MLNP algorithms that consider the global label hierarchy structure. We show that the joint posterior probability over all the node labels can be efficiently maximized by dynamic programming for label trees, or greedy algorithm for label DAGs. In addition, both algorithms can be further extended for the minimization of the expected symmetric loss. Experiments are performed on real-world MLNP data sets with label trees and label DAGs. The proposed method consistently outperforms other hierarchical and flat multilabel classification methods.
机译：在分层分类中，输出标签位于树形或有向无环图（DAG）结构的分层结构中。在测试时，可能需要给定测试示例的预测路径终止于标签层次结构的叶节点。这被称为强制性叶子节点预测（MLNP），当叶子节点比内部节点具有更强的语义时，它特别有用。但是，尽管在分层多类分类中有很多MLNP方法，但是在分层多标签分类中执行MLNP却很困难。在本文中，我们提出了考虑全局标签层次结构的新颖MLNP算法。我们表明，可以通过标签树的动态编程或标签DAG的贪​​婪算法有效地最大化所有节点标签上的联合后验概率。另外，两种算法都可以进一步扩展以最小化预期的对称损耗。对带有标签树和标签DAG的实际MLNP数据集进行了实验。所提出的方法始终优于其他分层和平坦的多标签分类方法。

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