Yanshan Xiao , Mengyue Zeng , Bo Liu , Liang Zhao , Xiangjun Kong , Zhifeng Hao
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引用次数: 0
摘要
序数回归(Ordinal Regression,OR)处理的是按顺序排列类别的分类问题。目前,大多数正序回归方法都是针对单个任务设计的,对多任务正序回归的研究还很有限。这些多任务回归方法假定不同任务具有相同的相关性,对整体模型的贡献相同。然而,在实践中,不同的任务可能与整体模型有不同的相关性。如果对它们一视同仁,整体模型的性能可能会受到限制。在本文中,我们提出了一种带有任务权重发现(MORTD)的新型多任务 OR 方法。我们为每个任务分配一个权重,表明其与整体模型的相关性。根据任务权重,我们构建了一个最大裕度多任务 OR 模型。然后,我们采用启发式框架来构建多任务 OR 分类器,并交替更新任务权重。在这个框架中,双坐标下降法被用来高效地训练多任务 OR 分类器。在现实世界的 OR 应用中,多个任务的相关性可能并不完全相同。MORTD 的贡献在于它能发现任务的权重,从而产生更精确的分类模型。在现实生活中的 OR 数据集上进行的大量实验表明,与现有的多任务 OR 方法相比,MORTD 能够提供更高的分类精度,同时所需的训练时间也更短。
Multi-task ordinal regression with task weight discovery
Ordinal regression (OR) deals with the classification problems that the classes are ranked in order. At present, most OR approaches are designed for individual tasks, the research on multi-task OR is limited. These multi-task OR approaches assume that different tasks have the same relatedness and contribute equally to the overall model. However, in practice, different tasks may have distinct relatedness to the overall model. If they are treated equally, the performance of the overall model may be restricted. In this paper, we propose a novel multi-task OR approach with task weight discovery (MORTD). We assign each task a weight that indicates its relatedness to the overall model. Based on the task weights, a maximum margin multi-task OR model is constructed. Then, we adopt a heuristic framework to construct the multi-task OR classifier and update the task weights alternately. In this framework, the dual coordinate descent method is adapted to train the multi-task OR classifier efficiently. In real-world OR applications, the relatedness of multiple tasks may not be exactly the same. The contribution of MORTD is that it can discover the weights of tasks to yield a more precise classification model. Substantial experiments on real-life OR datasets illustrate that compared to the existing multi-task OR methods, MORTD is able to deliver higher classification accuracy and meanwhile needs less training time.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.