Multi-Label Classification with Feature-Aware Cost-Sensitive Label Embedding

Hsien-Chun Chiu, Hsuan-Tien Lin
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引用次数: 1

Abstract

Multi-label classification (MLC) is an important learning problem where each instance is annotated with multiple labels. Label embedding (LE) is an important family of methods for MLC that extracts and utilizes the latent structure of labels towards better performance. Within the family, feature-aware LE methods, which jointly consider the feature and label information during extraction, have been shown to reach better performance than feature-unaware ones. Nevertheless, current feature-aware LE methods are not designed to flexibly adapt to different evaluation criteria. In this work, we propose a novel feature-aware LE method that takes the desired evaluation criterion (cost) into account during training. The method, named Feature-aware Cost-sensitive Label Embedding (FaCLE), encodes the criterion into the distance between embedded vectors with a deep Siamese network. The feature-aware characteristic of FaCLE is achieved with a loss function that jointly considers the embedding error and the feature-to-embedding error. Moreover, FaCLE is coupled with an additional-bit trick to deal with the possibly asymmetric criteria. Experiment results across different data sets and evaluation criteria demonstrate that FaCLE is superior to other state-of-the-art feature-aware LE methods and competitive to cost-sensitive LE methods.
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基于特征感知成本敏感标签嵌入的多标签分类
多标签分类(MLC)是一个重要的学习问题,每个实例都用多个标签进行标注。标签嵌入(Label embedding, LE)是一种重要的MLC方法,它可以提取和利用标签的潜在结构来获得更好的性能。在该家族中,特征感知的LE方法在提取过程中共同考虑特征和标签信息,已被证明比特征不感知的LE方法达到更好的性能。然而,目前的特征感知LE方法并不能灵活地适应不同的评价标准。在这项工作中,我们提出了一种新的特征感知LE方法,该方法在训练过程中考虑了所需的评估标准(成本)。该方法被称为特征感知成本敏感标签嵌入(FaCLE),该方法利用深度暹罗网络将标准编码为嵌入向量之间的距离。FaCLE的特征感知特性是通过一个综合考虑嵌入误差和特征到嵌入误差的损失函数来实现的。此外,FaCLE与一个额外的位技巧相结合,以处理可能的不对称标准。不同数据集和评估标准的实验结果表明,FaCLE优于其他最先进的特征感知LE方法,并具有成本敏感LE方法的竞争力。
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