Distributed Online Ordinal Regression Based on VUS Maximization

被引：0

作者：

Liu, Huan ^{[1
]}

Tu, Jiankai ^{[1
]}

Li, Chunguang ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2024年 / 31卷

基金：

中国国家自然科学基金;

关键词：

Distributed databases; Linear programming; Loss measurement; Diseases; Classification algorithms; Approximation methods; Volume measurement; Ordinal regression; ROC; VUS; distributed learning; online learning;

D O I：

10.1109/LSP.2024.3456629

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：2395 / 2399

页数：5

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