[1]周博文,熊伟丽.采用双层优选策略的主动学习算法及其应用[J].智能系统学报,2022,17(4):688-697.[doi:10.11992/tis.202106041]
ZHOU Bowen,XIONG Weili.Active learning algorithm and its application based on a two-tier optimization strategy[J].CAAI Transactions on Intelligent Systems,2022,17(4):688-697.[doi:10.11992/tis.202106041]
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ZHOU Bowen,XIONG Weili.Active learning algorithm and its application based on a two-tier optimization strategy[J].CAAI Transactions on Intelligent Systems,2022,17(4):688-697.[doi:10.11992/tis.202106041]
采用双层优选策略的主动学习算法及其应用
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第4期
页码:
688-697
栏目:
学术论文—机器学习
出版日期:
2022-07-05
- Title:
- Active learning algorithm and its application based on a two-tier optimization strategy
- Keywords:
- active learning; two-tier optimization; sample uncertainty; distribution information; evaluation indicator; redundant information; modeling application; debutanizer
- 分类号:
- TP274
- 摘要:
- 针对工业生产过程中有标签样本少而人工标记代价高的问题，提出一种基于双层优选策略的主动学习算法。首先，建立不同预测模型对无标签样本的信息量进行评估；其次，充分考虑样本的分布信息，从样本的不确定性、差异性和代表性3个角度出发，提出新的评价指标，优选无标签样本，并去除冗余信息；最后，对双层优选的样本进行人工标记，重构有标签样本集后进行建模应用。通过脱丁烷塔的工业过程数据进行算法的应用仿真，验证了所提算法的有效性与性能。
- Abstract:
- Aiming at the problem that the number of label samples is small and the cost of manual labeling is high in the industrial production process, an active learning algorithm based on a two-tier optimization strategy is proposed. First, establish different prediction models to evaluate the amount of information contained in unlabeled samples; secondly, fully consider the distribution information of the samples and, from the three perspectives of sample uncertainty, difference, and representativeness, propose new evaluation indicators, preferably unlabeled samples, and remove redundant information; finally, the double-layered preferred samples are manually labeled, and the labeled sample set is reconstructed for modeling application. The application simulation of the algorithm through the industrial process data of the debutanizer verifies the effectiveness and performance of the proposed algorithm.
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备注/Memo
收稿日期:2021-06-25。
基金项目:国家自然科学基金项目(61773182)； 国家重点研发计划子项目(2018YFC1603705-03).
作者简介:周博文，硕士研究生，主要研究方向为复杂工业过程建模;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与监控、智能软测量技术。主持国家自然科学基金面上项目、国家自然科学基金青年项目、江苏省产学研等省部级以上纵向项目8项，授权发明专利近20项。发表学术论文90余篇，获中国商业联合会科技进步一等奖1项
通讯作者:熊伟丽. E-mail：greenpre@163.com
更新日期/Last Update:
1900-01-01