论文标题
基于相似性的分类框架
A Similarity-based Framework for Classification Task
论文作者
论文摘要
基于相似性的方法产生了一类新的多标签学习方法，还可以实现有希望的性能。在本文中，我们概括了此方法，从而为分类任务提供了新的框架。具体而言，我们将基于相似性的学习和广义线性模型团结起来，以实现两全其美的最佳状态。这使我们能够捕获班级之间的相互依赖性，并防止噪音类的性能损害。该模型的每个学习参数都可以揭示一个类对另一类的贡献，从而在某种程度上提供了解释性。实验结果显示了所提出的方法对多级和多标签数据集的有效性
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite similarity-based learning and generalized linear models to achieve the best of both worlds. This allows us to capture interdependencies between classes and prevent from impairing performance of noisy classes. Each learned parameter of the model can reveal the contribution of one class to another, providing interpretability to some extent. Experiment results show the effectiveness of the proposed approach on multi-class and multi-label datasets
