广东工业大学学报2025，Vol.42Issue(4)：1-7,7.DOI:10.12052/gdutxb.250108
面向标签可变性的可拓分类方法
Extension Classification Method for Label Variability
摘要
Abstract
Traditional classification algorithms typically assume that the labels of training samples are static and deterministic,ignoring the dynamic characteristics of sample labels that may change with conditions in real-world scenarios.In response to this issue,this paper proposes a new learning problem setting—the Extended Classification Problem,which simultaneously gives the class labels and label variability states of samples in the training data,which to characterize the class transition potential of samples under the influence of change mechanisms.Based on this setting,a multi-label learning framework was designed,an extension classification algorithm for label variability using support vector machine was constructed,to achieve collaborative optimization of category discrimination and label variability prediction.The experimental section validated the effectiveness of the proposed algorithm on both synthetic and real datasets.This paper provides a new modeling approach for label dynamic learning problems,which has good application prospects.关键词
可拓分类/可拓学/标签可变性/多标签学习/支持向量机Key words
extensible classification/Extenics/label variability/multi-label learning/support vector machine分类
信息技术与安全科学引用本文复制引用
田英杰,刘大莲,李兴森..面向标签可变性的可拓分类方法[J].广东工业大学学报,2025,42(4):1-7,7.基金项目
国家自然科学基金资助项目(71731009,72071049) （71731009,72071049）
广东省自然科学基金资助项目(2024A1515011324) （2024A1515011324）
北京联合大学校级科研项目(ZK20202507) （ZK20202507）
