桂林电子科技大学学报2024，Vol.44Issue(6)：551-559,9.DOI:10.16725/j.1673-808X.2023118
基于kNCN的标签噪声在线核学习方法
An online kernel learning method for label noise based on kNCN
摘要
Abstract
Kernel methods have been developed to handle nonlinear classification problems in online classification.In recent years,budget maintenance algorithms have been developed to avoid the infinite increase in the number of support vectors with data flow when calculating kernel functions.The existing fixed budget kernel classification algorithms are severely affected by label noise in classification performance.To address this issue,a label noise online kernel learning method based on kNCN is proposed.When the buffer reaches the budget size,this method uses the kNCN principle to find k near centroid nearest neighbor points for each support vector in the buffer.Then,by calculating the local label inconsistency between them,the deletion candidate set and anchor set are constructed.Then,a trial and error model is established for all instances in the deletion candidate set,and the classification accuracy of the trial and error model is tested on the anchor set to determine which support vector should be most removed from the buffer,maintain a fixed budget.Experimental results on synthetic data sets and real data sets show that the application of this method on fixed-budget perceptrons and passive attack algorithms can effectively improve classification performance in label noise scenarios.The comprehensive results on six data sets Ranking is better than other comparison algorithms.关键词
预算维护/近质心近邻/核方法/在线分类/标签噪声Key words
budget maintenance/near centroid nearest neighbor/kernel methods/online classification/label noise分类
信息技术与安全科学引用本文复制引用
李世鑫,文益民..基于kNCN的标签噪声在线核学习方法[J].桂林电子科技大学学报,2024,44(6):551-559,9.基金项目
广西重点研发计划(桂科AB21220023) （桂科AB21220023）
国家自然科学基金(61866007) （61866007）
广西图像图形与智能处理重点实验室基金(GIIP2005) （GIIP2005）
