[1]程康明,熊伟丽.一种自训练框架下的三优选半监督回归算法[J].智能系统学报,2020,15(3):568-577.[doi:10.11992/tis.201905033]
 CHENG Kangming,XIONG Weili.Three-optimal semi-supervised regression algorithm under self-training framework[J].CAAI Transactions on Intelligent Systems,2020,15(3):568-577.[doi:10.11992/tis.201905033]
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一种自训练框架下的三优选半监督回归算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年3期
页码:
568-577
栏目:
学术论文—机器学习
出版日期:
2020-09-05

文章信息/Info

Title:
Three-optimal semi-supervised regression algorithm under self-training framework
作者:
程康明1 熊伟丽2
1. 江南大学 物联网工程学院,江苏 无锡 214122;
2. 江南大学 轻工过程先进控制教育部实验室,江苏 无锡 214122
Author(s):
CHENG Kangming1 XIONG Weili2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China
关键词:
工业生产无标签样本优选半监督回归相似性高斯过程回归置信度判断自训练预测
Keywords:
industrial productionunlabeled samplesfiltersemi-supervised regressionsimilarityGaussian process regressionconfidence judgmentself-trainingprediction
分类号:
TP274
DOI:
10.11992/tis.201905033
摘要:
工业生产过程数据由于主导变量分析代价等因素可能出现有标签样本少而无标签样本多的情况,为提升对无标签样本利用的准确性与充分性,提出一种自训练框架下的三优选半监督回归算法。对无标签样本与有标签样本进行优选,保证两类数据的相似性,以提高无标签样本预测的准确性;利用高斯过程回归方法对所选有标签样本集建模,预测所选无标签样本集,得到伪标签样本集;通过对伪标签样本集置信度进行判断,优选出置信度高的样本用于更新初始样本集;为了进一步提高无标签样本利用的充分性,在自训练框架下,进行多次循环筛选提高无标签样本的利用率。通过对脱丁烷塔过程实际数据的建模仿真,验证了所提方法在较少有标签样本情况下的良好预测性能。
Abstract:
In industrial production, due to factors such as the cost of analyzing the dominant variable, there may be cases in which there are few labeled and many unlabeled samples. To improve performance and accuracy in the use of unlabeled samples, we propose the use of a three-optimal semi-supervised regression algorithm under a self-training framework. This algorithm first filters unlabeled and labeled samples to ensure similarity between these two types of data and improve the accuracy of predicting the unlabeled samples. Then, a model is established based on the selected labeled samples using Gaussian process regression to predict the unlabeled samples, from which pseudo-label samples are obtained. Then, by determining the confidence levels of the prediction of the pseudo-label samples, samples with higher confidence levels are filtered and used to update the initial samples. Finally, through multiple filtering loops, a self-training framework is applied to improve the utilization of unlabeled samples. By modeling and simulating debutanizer process data, the proposed method was confirmed to have superior prediction performance when there are an insufficient number of labeled samples.

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相似文献/References:

[1]程康明,熊伟丽.一种双优选的半监督回归算法[J].智能系统学报,2019,14(04):689.[doi:10.11992/tis.201805010]
 CHENG Kangming,XIONG Weili.A dual-optimal semi-supervised regression algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(3):689.[doi:10.11992/tis.201805010]

备注/Memo

备注/Memo:
收稿日期:2019-05-15。
基金项目:国家自然科学基金项目(61773182);江苏省自然科学基金项目(BK20170198)
作者简介:程康明,硕士研究生,主要研究方向为工业过程建模、机器学习和大数据分析;熊伟丽,教授,博士生导师,主要研究方向为复杂工业过程建模及优化、智能优化算法及应用。入选江苏省“青蓝工程”中青年学术带头人。主持国家自然科学基金面上项目、江苏省产学研等纵向项目8项;参与国家863计划、重点研发计划等多项。发 表学术论文60余篇
通讯作者:熊伟丽.E-mail:greenpre@163.com
更新日期/Last Update: 1900-01-01