Tackle balancing constraints in semi-supervised ordinal regression

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chenkang Zhang, Heng Huang, Bin Gu
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引用次数: 0

Abstract

Semi-supervised ordinal regression (S2OR) has been recognized as a valuable technique to improve the performance of the ordinal regression (OR) model by leveraging available unlabeled samples. The balancing constraint is a useful approach for semi-supervised algorithms, as it can prevent the trivial solution of classifying a large number of unlabeled examples into a few classes. However, rapid training of the S2OR model with balancing constraints is still an open problem due to the difficulty in formulating and solving the corresponding optimization objective. To tackle this issue, we propose a novel form of balancing constraints and extend the traditional convex–concave procedure (CCCP) approach to solve our objective function. Additionally, we transform the convex inner loop (CIL) problem generated by the CCCP approach into a quadratic problem that resembles support vector machine, where multiple equality constraints are treated as virtual samples. As a result, we can utilize the existing fast solver to efficiently solve the CIL problem. Experimental results conducted on several benchmark and real-world datasets not only validate the effectiveness of our proposed algorithm but also demonstrate its superior performance compared to other supervised and semi-supervised algorithms

Abstract Image

解决半监督序数回归中的平衡约束问题
半监督序数回归(S2OR)已被公认为是一种有价值的技术,可以利用现有的未标记样本提高序数回归(OR)模型的性能。对于半监督算法来说,平衡约束是一种有用的方法,因为它可以避免将大量未标记样本归入少数几个类别的琐碎解决方案。然而,由于难以制定和求解相应的优化目标,快速训练具有平衡约束的 S2OR 模型仍是一个未决问题。为了解决这个问题,我们提出了一种新的平衡约束形式,并扩展了传统的凸-凹过程(CCCP)方法来求解我们的目标函数。此外,我们将 CCCP 方法生成的凸内循环 (CIL) 问题转化为类似于支持向量机的二次问题,其中多个相等约束被视为虚拟样本。因此,我们可以利用现有的快速求解器来高效解决 CIL 问题。在多个基准数据集和现实世界数据集上进行的实验结果不仅验证了我们提出的算法的有效性,还证明了它与其他监督和半监督算法相比的卓越性能
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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