Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor () is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.

中文翻译：

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融合标准和有序 dropout 技术来正则化深度模型

Dropout 是一种流行的深度神经分类器正则化工具，但无论分类任务的性质如何：名义任务或序数任务，都可以应用它。因此，序数问题的类标签之间的顺序关系被忽略。在本文中，我们提出了标准 dropout 和一种新的 dropout 方法的融合，用于序数分类正则化深度神经网络，以避免过度拟合并提高泛化能力，但考虑到序数任务的额外信息，这些信息可用于提高性能。每个神经元的输出与目标标签之间的相关性用于指导丢弃过程：神经元与预期标签的相关性越高，其被丢弃的概率越低。鉴于随机性在正则化过程中也起着至关重要的作用，在训练过程中还添加了一个平衡因子（）来确定序数相对于常数概率的影响，从而提供了一种混合序数正则化方法。大量实验表明，新的混合序数 dropout 方法比标准 dropout 表现更好，在大多数评估指标中获得了改进的结果，不仅包括序数指标，还包括名义指标。