Multilabel classification by exploiting data‐driven pair‐wise label dependence
Tao He, Lei Zhang, Jixiang Guo, Yi Zhang · International Journal of Intelligent Systems · 2020
Exploiting label dependence is a widely used approach to boost classification performance for multilabel classification problems. However, most of the traditional label dependence methods have high time complexity, especially when combined with deep neural networks (DNNs). Thus they usually can not be efficiently applied in large-scale data sets. Recent advances in large-scale multilabel classification widely developed pair-wise ranking and structure-driven methods, but label dependence was little exploited. In most of the structure-driven methods, binary relevance (BR) with multiple binary cross-entropy (BCE) loss functions, a simple but effective method, is still the prior solution incorporation with DNNs in large-scale data sets. In this paper, we propose a novel loss function called label dependent cross-entropy (LDCE), which directly introduces label dependence to BCE loss function by data-driven conditional probability. Combined with deep convolutional neural networks (DCNNs), LDCE introduces no extra parameters and induces very little extra computational complexity. Moreover, we develop its tiny variant with sparse label dependence and its learnable version for automatic learning pair-wise label dependence. Within the BR scheme, LDCE outperforms BCE on seven widely used benchmark datasets. We also perform two large-scale multilabel image classification tasks (VOC 2007 and ChestX-ray14) with DCNNs, and LDCE outperforms BCE and achieves comparable results to the state-of-the-art.