Label Distribution Learning (LDL) is a new learning paradigm with numerous applications in various domains. It is a generalization of both standard multiclass classification and multilabel classification. Instead of a binary value, in LDL, each label is assigned a real number which corresponds to a degree of membership of the object being classified to a given class. In this paper a new neural network approach to Label Distribution Learning (Duo-LDL), which considers pairwise class dependencies, is introduced. The method is extensively tested on 15 well-established benchmark sets, against 6 evaluation measures, proving its competitiveness to state-of-the-art non-neural LDL approaches. Additional experimental results on artificially generated data demonstrate that Duo-LDL is especially effective in the case of most challenging benchmarks, with extensive input feature representations and numerous output classes.

标签分布学习 (LDL) 是一种新的学习范式，在各个领域都有大量应用。它是标准多类分类和多标签分类的概括。在 LDL 中，不是二进制值，而是为每个标签分配一个实数，该实数对应于被分类到给定类的对象的隶属度。在本文中，介绍了一种新的标签分布学习 (Duo-LDL) 神经网络方法，该方法考虑了成对的类依赖性。该方法在 15 个完善的基准集上进行了广泛测试，对照 6 个评估措施，证明了其与最先进的非神经 LDL 方法的竞争力。人工生成数据的其他实验结果表明，Duo-LDL 在最具挑战性的基准测试中特别有效，