基于任务权重自动优化的多任务序数回归算法  

Multi-task ordinal regression with task weight discovery

作　　者：曾梦岳 肖燕珊[1] 刘波[2] Zeng Mengyue;Xiao Yanshan;Liu Bo(School of Computer Science&Technology,Guangdong University of Technology,Guangzhou 510006,China;School of Automation,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]广东工业大学自动化学院,广州510006

出　　处：《计算机应用研究》2024年第4期1052-1057,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(62076074)。

摘　　要：目前,只有少量面向多任务学习的序数回归方法。这些方法假设不同的任务具有相同的权重,对整体模型具有相同的贡献。然而,在真实应用中,不同任务对于整体模型的贡献往往是不同的。为此,提出了一种基于任务权重自动优化的多任务序数回归算法。首先,提出了基于支持向量机的多任务序数回归模型,通过分类器参数共享,实现不同任务之间的信息迁移;其次,考虑到不同任务对整体模型可能具有不同贡献,赋予每个任务一个权重,这些权重将在学习过程中自动优化求解;最后,采用了启发式框架,交替地建立多任务序数回归模型和优化任务权重。实验结果表明,提出方法相比于其他多任务序数回归方法,平均0-1误差降低了3.8%~12.3%,平均绝对误差降低了4.1%~11%。考虑了每个任务的不同权重,通过自动优化这些权重,降低了多任务序数回归模型的分类误差。At present,there are only a very few works done on multi-task ordinal regression(OR).These works assume that different tasks contribute equally to the overall model.However,in practice,different tasks may have distinct contributions to the overall model.This paper proposed a novel multi-task ordinal regression method with task weight discovery method.Firstly,it presented a support-vector-machine-based multi-task OR model.By sharing the classifier parameters,the classification information could be transferred among different tasks.Secondly,considering that different tasks had different contributions to the overall model,it assigned each task a weight,which would be automatically optimized during the learning process.Finally,it adopted a heuristic framework to construct the multi-task OR model and optimized the task weights alternately.The experimental results show that the proposed method achieves 3.8%to 12.3%improvements in terms of MZE and 4.1%to 11%improvements in terms of MAE,compared to the existing multi-task OR methods.Considering the different weights of each task,and by automatically optimizing these weights,the proposed method reduces the classification error of the multi-task ordinal regression model.

关 键 词：序数回归 多任务学习 权重优化 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O212.1[自动化与计算机技术—控制科学与工程]