In recent times, deep neural networks achieved outstanding predictive performance on various classification and pattern recognition tasks. However, many real-world prediction problems have ordinal response variables, and this ordering information is ignored by conventional classification losses such as the multi-category cross-entropy. Ordinal regression methods for deep neural networks address this. One such method is the CORAL method, which is based on an earlier binary label extension framework and achieves rank consistency among its output layer tasks by imposing a weight-sharing constraint. However, while earlier experiments showed that CORAL's rank consistency is beneficial for performance, {it is limited by a weight-sharing constraint in a neural network's fully connected output layer. We propose a new method for rank-consistent ordinal regression without this limitation. Our rank-consistent ordinal regression framework (CORN) achieves rank consistency by a novel training scheme. This training scheme uses} conditional training sets to obtain the unconditional rank probabilities through applying the chain rule for conditional probability distributions. Experiments on various datasets demonstrate the efficacy of the proposed method to utilize the ordinal target information, and the absence of the weight-sharing restriction improves the performance substantially compared to the CORAL reference approach.
翻译:近些年来,深神经网络在各种分类和模式识别任务上取得了杰出的预测性业绩。然而,许多现实世界的预测问题有正反响变量,而这种定序信息被多类跨天体等常规分类损失忽略。深神经网络的奥氏回归方法就解决了这一点。一种方法就是CORAL方法,它基于早期的二进制标签扩展框架,通过实行权重分担限制,在产出层任务中达到等级一致。然而,虽然早先的实验表明,CORAL的等级一致性有利于绩效,但{由于神经网络完全连接的产出层的权重分担限制而受到限制。我们提出了一种不设此限制的级同级反回归新方法。我们的级一致或非常规回归框架(CORN)通过一个新的培训计划实现了等级一致。这一培训计划使用有条件的培训组,通过对有条件的概率分布适用链规则来获得无条件的等级概率。在各种数据集上进行的实验表明,拟议的方法对于利用权重分配方法大幅改进CAL基准参考度的效能。