IEEE Trans Cybern. 2022 Sep;52(9):9546-9558. doi: 10.1109/TCYB.2021.3059631. Epub 2022 Aug 18.
Hierarchical structures of labels usually exist in large-scale classification tasks, where labels can be organized into a tree-shaped structure. The nodes near the root stand for coarser labels, while the nodes close to leaves mean the finer labels. We label unseen samples from the root node to a leaf node, and obtain multigranularity predictions in the hierarchical classification. Sometimes, we cannot obtain a leaf decision due to uncertainty or incomplete information. In this case, we should stop at an internal node, rather than going ahead rashly. However, most existing hierarchical classification models aim at maximizing the percentage of correct predictions, and do not take the risk of misclassifications into account. Such risk is critically important in some real-world applications, and can be measured by the distance between the ground truth and the predicted classes in the class hierarchy. In this work, we utilize the semantic hierarchy to define the classification risk and design an optimization technique to reduce such risk. By defining the conservative risk and the precipitant risk as two competing risk factors, we construct the balanced conservative/precipitant semantic (BCPS) risk matrix across all nodes in the semantic hierarchy with user-defined weights to adjust the tradeoff between two kinds of risks. We then model the classification process on the semantic hierarchy as a sequential decision-making task. We design an algorithm to derive the risk-minimized predictions. There are two modules in this model: 1) multitask hierarchical learning and 2) deep reinforce multigranularity learning. The first one learns classification confidence scores of multiple levels. These scores are then fed into deep reinforced multigranularity learning for obtaining a global risk-minimized prediction with flexible granularity. Experimental results show that the proposed model outperforms state-of-the-art methods on seven large-scale classification datasets with the semantic tree.
层次结构的标签通常存在于大规模分类任务中，其中标签可以组织成树状结构。根节点附近的节点表示较粗的标签，而靠近叶子的节点表示较细的标签。我们从根节点开始对未标记的样本进行标记，直到到达叶节点，从而在层次分类中获得多粒度的预测。有时，由于不确定性或信息不完整，我们无法获得叶决策。在这种情况下，我们应该在内部节点停止，而不是贸然前进。然而，大多数现有的层次分类模型旨在最大化正确预测的百分比，而不考虑错误分类的风险。在一些实际应用中，这种风险至关重要，可以通过类别层次结构中真实值与预测值之间的距离来衡量。在这项工作中，我们利用语义层次结构来定义分类风险，并设计了一种优化技术来降低这种风险。通过将保守风险和激进风险定义为两个竞争风险因素，我们在语义层次结构的所有节点上构建了平衡的保守/激进语义（BCPS）风险矩阵，并使用用户定义的权重来调整两种风险之间的权衡。然后，我们将分类过程建模为一个顺序决策任务。我们设计了一种算法来得出风险最小化的预测。该模型有两个模块：1）多任务层次学习和 2）深度强化多粒度学习。第一个模块学习多个层次的分类置信度得分。然后，这些分数被输入到深度强化多粒度学习中，以获得具有灵活粒度的全局风险最小化预测。实验结果表明，该模型在七个具有语义树的大规模分类数据集上的表现优于最先进的方法。
