Algorithm Research & Explore
|
365-370,387

Unsupervised transfer learning Boosting for weight optimization under multi-source domain distribution

Li Yunboa
Wang Shitongb
a. School of Artificial Intelligence & Computer Science, b. Jiangsu Key Construction Laboratory of IoT Application Technology, Jiangnan University, Wuxi Jiangsu 214122, China

Abstract

The deep decision tree migration learning boosting method(DtrBoost) can effectively realize the migration learning from a single source domain to a target domain under supervision, but can not realize the unsupervised migration scenario under multiple source domains. To solve this problem, this paper proposed an unsupervised transfer learning boosting method for optimizing the weight under multi-source domain distribution. The main idea was to calculate the corresponding KL value according to the distribution of different source domains and target domains, selected an appropriate number of samples from different source domains to train the classifier and pseudo label the samples from the target domain. Finally, the algorithm assigned different learning weights according to the KL distance of each different source domain, and the labeled source domain samples integrated to the pseudo labeled target domain to obtain the final result. Comparative experiments show that the proposed algorithm achieves better classification accuracy and adaptive effect on different data sets. The average classification error rate decreases by 2.4% and more than 6% on the best marketing data set.

Foundation Support

国家自然科学基金资助项目(61972181)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0327
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 365-370,387
Serial Number: 1001-3695(2023)02-008-0365-06

Publish History

[2022-09-28] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

李赟波, 王士同. 多源域分布下优化权重的无监督迁移学习Boosting方法 [J]. 计算机应用研究, 2023, 40 (2): 365-370,387. (Li Yunbo, Wang Shitong. Unsupervised transfer learning Boosting for weight optimization under multi-source domain distribution [J]. Application Research of Computers, 2023, 40 (2): 365-370,387. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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