Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes. In the deep learning era, extensive efforts have been spent on developing more powerful neural embedding models to better represent the instance for improving MCC performance. In this paper, we do not aim to propose new neural models for instance representation learning, but to show that it is promising to boost MCC performance with a novel formulation through the lens of ranking. In particular, by viewing MCC as to rank classes for an instance, we first argue that ranking metrics, such as Normalized Discounted Cumulative Gain, can be more informative than the commonly used Top-$K$ metrics. We further demonstrate that the dominant neural MCC recipe can be transformed to a neural ranking framework. Based on such generalization, we show that it is intuitive to leverage advanced techniques from the learning to rank literature to improve the MCC performance out of the box. Extensive empirical results on both text and image classification tasks with diverse datasets and backbone neural models show the value of our proposed framework.

翻译：多级分类(MCC)是将每例分类为一套预先界定的类别的基本机器学习问题。在深层次学习时代,已经为开发更强大的神经嵌入模型做出了大量努力,以更好地代表改善MCC绩效的范例。在本文中,我们的目的不是提出新的神经模型,例如代表性学习,而是表明它有望通过从排名的角度来看新颖的配方来提升CC的性能。特别是,通过将CCC看成一个等级,我们首先认为,定级指标,例如普通化的折扣累积增益,比通常使用的顶值-K$公元衡量标准更具有信息性。我们进一步证明,占支配地位的神经控制器配方可以转换为神经排序框架。基于这种概括性,我们表明,利用从学习到文学的先进技术来提高MCC的性能是明智的。在文本和图像分类任务上,通过多种数据集和骨干神经模型的广泛经验结果可以显示我们提议的框架的价值。