{"title":"Distributed Online Ordinal Regression Based on VUS Maximization","authors":"Huan Liu;Jiankai Tu;Chunguang Li","doi":"10.1109/LSP.2024.3456629","DOIUrl":null,"url":null,"abstract":"Ordinal regression (OR) is a multi-class classification problem with ordered labels. The objective functions of most OR methods are based on the misclassification error. The volume under the ROC surface (VUS) is a measure of OR that quantifies the ranking ability of OR models. It can also be used as an objective function in OR. In practice, data may be collected by multiple nodes in a distributed and online manner, and is difficult to process centrally. In this paper, we intend to develop a VUS-based distributed online OR method. Computing VUS requires a sequence of data from all categories, but the available online data may not cover all categories and the required data may distribute across different nodes. Besides, the existing approximation methods of VUS are inappropriate for using in OR. To address these issues, we first propose two new surrogate losses of the VUS in OR. We then derive their decomposed formulations and propose distributed online OR algorithms based on VUS maximization (dVMOR). The experimental results demonstrate their effectiveness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10670002/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Ordinal regression (OR) is a multi-class classification problem with ordered labels. The objective functions of most OR methods are based on the misclassification error. The volume under the ROC surface (VUS) is a measure of OR that quantifies the ranking ability of OR models. It can also be used as an objective function in OR. In practice, data may be collected by multiple nodes in a distributed and online manner, and is difficult to process centrally. In this paper, we intend to develop a VUS-based distributed online OR method. Computing VUS requires a sequence of data from all categories, but the available online data may not cover all categories and the required data may distribute across different nodes. Besides, the existing approximation methods of VUS are inappropriate for using in OR. To address these issues, we first propose two new surrogate losses of the VUS in OR. We then derive their decomposed formulations and propose distributed online OR algorithms based on VUS maximization (dVMOR). The experimental results demonstrate their effectiveness.
序数回归（Ordinal regression，OR）是一种具有有序标签的多类分类问题。大多数序列回归方法的目标函数都基于误分类误差。ROC 表面下的体积（VUS）是对 OR 的一种度量，它量化了 OR 模型的排序能力。它也可用作 OR 的目标函数。在实践中，数据可能由多个节点以分布式在线方式收集，难以集中处理。本文打算开发一种基于 VUS 的分布式在线 OR 方法。计算 VUS 需要所有类别的数据序列，但可用的在线数据可能无法覆盖所有类别，所需的数据也可能分布在不同的节点上。此外，现有的 VUS 近似方法也不适合用于 OR。为了解决这些问题，我们首先提出了两个新的替代损失。然后，我们推导出它们的分解式，并提出了基于 VUS 最大化的分布式在线 OR 算法（dVMOR）。实验结果证明了它们的有效性。
期刊介绍：
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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