基于分类风险的半监督集成学习算法

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万方数据
维普

中文摘要：针对当前半监督集成学习算法对无标记样本预测时容易出现的标注混沌问题,文中提出基于分类风险的半监督集成学习算法(Classification Risk-Based Semi-supervised Ensemble Learning Algorithm,CR-SSEL).采用分类风险作为无标记样本置信度的评判标准,可有效衡量样本标注的不确定性程度.迭代地训练分类器,对高置信度样本进行再强化,使样本标注的不确定性逐渐降低,增强半监督集成学习算法的分类性能.在多个标准数据集上验证CR-SSEL的学习参数影响、训练过程收敛和泛化性能提升,实验表明随着基分类器个数的增加,CR-SSEL的训练过程呈收敛趋势,获得较优的分类精度.

外文标题：Classification Risk-Based Semi-supervised Ensemble Learning Algorithm

外文摘要：The existing semi-supervised ensemble learning algorithms commonly encounter the issue of information confusion in predicting unlabeled samples.To address this issue,a classification risk-based semi-supervised ensemble learning(CR-SSEL)algorithm is proposed.Classification risk is utilized as the criterion for evaluating the confidence of unlabeled samples.It can measure the degree of sample uncertainty effectively.By iteratively training classifiers and restrengthening the high confidence samples,the uncertainty of sample labeling is reduced and thus the classification performance of SSEL is enhanced.The impacts of learning parameters,training process convergence and improvement of generalization capability of CR-SSEL algorithm are verified on multiple standard datasets.The experimental results demonstrate that CR-SSEL algorithm presents the convergence trend of training process with an increase in the number of base classifiers and it achieves better classification accuracy.

外文关键词：

Semi-supervised Ensemble LearningEnsemble LearningSemi-supervised LearningClassification RiskUncertaintyConfidence Degree

作者：

何玉林、朱鹏辉、黄哲学、PHILIPPE Fournier-Viger

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作者单位：

人工智能与数字经济广东省实验室(深圳) 深圳 518107

深圳大学计算机与软件学院深圳 518060

关键词：

半监督集成学习集成学习半监督学习分类风险不确定性置信度

基金：

广东省自然科学基金面上项目广东省基础与应用基础研究基金粤深联合基金重点项目深圳市基础研究面上项目

项目编号：

2023A15150116672023B1515120020JCYJ20210324093609026

出版年：

2024

DOI：

10.16451/j.cnki.issn1003-6059.202404005

模式识别与人工智能

中国自动化学会,国家智能计算机研究开发中心,中国科学院合肥智能机械研究所

模式识别与人工智能

CSTPCD北大核心

影响因子：0.954

ISSN：1003-6059

年,卷(期)：2024.37(4)

参考文献量32