[1]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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Active learning algorithm and its application based on a two-tier optimization strategy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 688-697 Column: 学术论文—机器学习 Public date: 2022-07-05
Title:
Active learning algorithm and its application based on a two-tier optimization strategy
Author(s):
ZHOU Bowen1;  XIONG Weili1; 2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
Keywords:
active learning; two-tier optimization; sample uncertainty; distribution information; evaluation indicator; redundant information; modeling application; debutanizer
CLC:
TP274
DOI:
10.11992/tis.202106041
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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