Bayes-Optimal Hierarchical Multilabel Classification
Bayes-Optimal Hierarchical Multilabel Classification
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DOI：
10.1109/tkde.2015.2441707
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发表时间：
2015-11
影响因子：
8.9
通讯作者：
Wei Bi;J. Kwok
中科院分区：
文献类型：
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作者：
Wei Bi;J. Kwok
分层多标签分类允许样本属于驻留在层次结构上的多个类别标签，该层次结构可以是树或有向无环图（DAG）。然而，流行的分层损失函数，如H-损失，只能在树层次结构上定义（但不能在DAG上定义），并且也可能在层次结构底部附近对错误分类进行过度或过度惩罚。此外，如何利用损失函数进行分层多标签分类的研究相对较少。为了克服这些不足，我们首先提出了分层扩展的汉明损失和排名损失，在标签层次结构的每个节点的错误考虑。然后，我们首先训练一个与损失函数无关的通用学习模型。接下来，使用贝叶斯决策理论，我们开发贝叶斯最优预测，最小化训练模型的相应风险。在计算上，而不是需要一个详尽的总结和搜索的最佳多标签，由此产生的优化问题可以有效地解决了贪婪算法。在大量真实数据集上的实验结果表明，所提出的贝叶斯最优分类器的性能优于现有的方法。
Hierarchical multilabel classification allows a sample to belong to multiple class labels residing on a hierarchy, which can be a tree or directed acyclic graph (DAG). However, popular hierarchical loss functions, such as the H-loss, can only be defined on tree hierarchies (but not on DAGs), and may also under- or over-penalize misclassifications near the bottom of the hierarchy. Besides, it has been relatively unexplored on how to make use of the loss functions in hierarchical multilabel classification. To overcome these deficiencies, we first propose hierarchical extensions of the Hamming loss and ranking loss which take the mistake at every node of the label hierarchy into consideration. Then, we first train a general learning model, which is independent of the loss function. Next, using Bayesian decision theory, we develop Bayes-optimal predictions that minimize the corresponding risks with the trained model. Computationally, instead of requiring an exhaustive summation and search for the optimal multilabel, the resultant optimization problem can be efficiently solved by a greedy algorithm. Experimental results on a number of real-world data sets show that the proposed Bayes-optimal classifier outperforms state-of-the-art methods.